fuzzy neural network hybrid modelling for runoff estimation



Jitendra Sinha,Avinash Agarwal and R. K. Sahu River Flow Modelling Using Artificial Neural Network Jitendra Sinha,Avinash Agarwal and R. K. Sahu River Flow Modelling Using Artificial Neural Network Новинка

Jitendra Sinha,Avinash Agarwal and R. K. Sahu River Flow Modelling Using Artificial Neural Network

5787 руб.
Water is vital for life and the initial step in its management is to quantify the runoff produced in the catchment area due to rainfall. For stream flow measurement gauging of catchment is done. Because of the high cost involved in the setting up and maintenance of gauging stations, it is not possible to set up and maintain the stations over many locations for a long period of time. Thus, although many large catchments are gauged, a lot of small catchments still remain ungauged. The problem demands for modelling of stream flow. The traditional techniques of modelling not only require lengthy and reliable rainfall-runoff records, but also a procedure for updating the model parameters from time to time. Under such condition stream flow modelling is performed with reasonable accuracy by data driven artificial intelligence technique such as Artificial Neural Network. This book provides a comprehensive approach to stream flow modelling of Upper Kharun Catchment in Chhatttisgarh, India; involving two different methodologies of Artificial Neural Network. The book will be highly useful to the research scholars, academicians, hydrologists and water resources engineers.
Swapnali Kale,Mangal Patil and M. G. Shinde Runoff Estimation Modelling Swapnali Kale,Mangal Patil and M. G. Shinde Runoff Estimation Modelling Новинка

Swapnali Kale,Mangal Patil and M. G. Shinde Runoff Estimation Modelling

4500 руб.
Prediction of both the volume and rate of runoff from a watershed from a rainfall event is vital for good design of hydraulic structures. There are a variety of methods for the estimation of surface runoff. The SCS curve number is one of the most popular and widely used methods of them. Another popular method for the estimation of runoff is the Green-Ampt method. This study involves the prediction of surface runoff with these two popular methods and the comparison of the predicted runoff with the observed runoff. The study also involves the estimation of subsurface runoff and the contribution of subsurface runoff in the total runoff of the watershed. Thus it is concluded that for the estimation of surface runoff Green-Ampt model is better than SCS curve number model. As the Green-Ampt model is physical based model, it gives close resemblance with observed runoff.
Hojjat Adeli Computational Intelligence. Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing Hojjat Adeli Computational Intelligence. Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing Новинка

Hojjat Adeli Computational Intelligence. Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing

11232.84 руб.
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.
Olaf Wandel Robot Control using an Artificial Neural Network Olaf Wandel Robot Control using an Artificial Neural Network Новинка

Olaf Wandel Robot Control using an Artificial Neural Network

4714 руб.
Inhaltsangabe:Abstract: The aim of the project was to control three joints of an industrial robot in terms of its position, velocity and acceleration. The work considered the necessary hardware, principles of neural networks and controlling techniques. The hardware comprised of a robot with three DC-motors and three optical position encoders, a personal computer with a D/A card for voltage output to the robot and two D/D cards. One D/D card for receiving values from the optical encoders and one for timing. The basics of artificial neural network type perceptrons were described. The special features bias, output feedback, momentum term, adjustment of momentum factor and adjustment of learning rate for this artificial neural network type were considered. An introduction to learning and control structures using artificial neural networks were given. These were controller copying, direct modelling, direct inverse modelling, control with a model and an inverse model, forward and inverse modelling, control action feedback error learning, feedback error learning, learning and control using the plant’s Jacobian. The conversion of two learning and control structures, direct inverse modelling and control action feedback error learning, was implemented in software using „MS QuickBASIC 4.5“. One joint was controlled with a direct inverse model. One joint and all joints together were controlled with control action feedback error learning. The results of experiments with these learning and...
Keith Beven J. Rainfall-Runoff Modelling. The Primer Keith Beven J. Rainfall-Runoff Modelling. The Primer Новинка

Keith Beven J. Rainfall-Runoff Modelling. The Primer

8753.72 руб.
Rainfall-Runoff Modelling: The Primer, Second Edition is the follow-up of this popular and authoritative text, first published in 2001. The book provides both a primer for the novice and detailed descriptions of techniques for more advanced practitioners, covering rainfall-runoff models and their practical applications. This new edition extends these aims to include additional chapters dealing with prediction in ungauged basins, predicting residence time distributions, predicting the impacts of change and the next generation of hydrological models. Giving a comprehensive summary of available techniques based on established practices and recent research the book offers a thorough and accessible overview of the area. Rainfall-Runoff Modelling: The Primer Second Edition focuses on predicting hydrographs using models based on data and on representations of hydrological process. Dealing with the history of the development of rainfall-runoff models, uncertainty in mode predictions, good and bad practice and ending with a look at how to predict future catchment hydrological responses this book provides an essential underpinning of rainfall-runoff modelling topics. Fully revised and updated version of this highly popular text Suitable for both novices in the area and for more advanced users and developers Written by a leading expert in the field Guide to internet sources for rainfall-runoff modelling software
Derong Liu Fundamentals of Computational Intelligence. Neural Networks, Fuzzy Systems, and Evolutionary Computation Derong Liu Fundamentals of Computational Intelligence. Neural Networks, Fuzzy Systems, and Evolutionary Computation Новинка

Derong Liu Fundamentals of Computational Intelligence. Neural Networks, Fuzzy Systems, and Evolutionary Computation

9296.14 руб.
Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.
Haider Raza Fuzzy Spiking Neural Networks Haider Raza Fuzzy Spiking Neural Networks Новинка

Haider Raza Fuzzy Spiking Neural Networks

3752 руб.
Master's Thesis from the year 2011 in the subject Engineering - Computer Engineering, grade: 8.84, Manav Rachna International University, course: Master of Technology (M.Tech), language: English, abstract: This dissertation presents an introductory knowledge to computational neuroscience and major emphasize on the branch of computational neuroscience called Spiking Neural Networks (SNNs). SNNs are also called the third generation neural networks. It has become now a major field of Soft Computing. In this we talk about the temporal characteristics' of neuron and studied the dynamics of it. We have presented SNNs architecture with fuzzy reasoning capability. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train frequencies and behave in a similar manner as fuzzy membership functions. The network of SNNs consists of three layers that is input, hidden and output layer. The topology of this network is based on Radial basis Network, which can be regarded as universal approximators. The input layer receives the input in the form of frequency which produces the spikes through linear encoding. There is another method of encoding called Poisson encoding; this encoding is used where the data is large. The hidden layer use Receptive Field (RF) to process the input and thus it is frequency selective. The output layer is only responsible for learning. The learning is based on local learning. The XOR classific...
Ehab Saif Ghith,Moustafa Mohamed Eissa and Gurvinder S. Virk Induction Motor Speed Control Technique Using Intelligent Methods Ehab Saif Ghith,Moustafa Mohamed Eissa and Gurvinder S. Virk Induction Motor Speed Control Technique Using Intelligent Methods Новинка

Ehab Saif Ghith,Moustafa Mohamed Eissa and Gurvinder S. Virk Induction Motor Speed Control Technique Using Intelligent Methods

6190 руб.
Book relates to the speed control of an induction motor introduced intelligent methods such as Fuzzy Logic Control (FLC), Artificial Neural Networks (ANN), Adaptive Neural Fuzzy Inference System (ANFIS) and Optimization Techniques such as Genetic Algorithm (GA), Sequential Quadratic Programming (SQP) and Particle Swarm Optimization Algorithms(PSO).The results showed that the PSO-PI controller can perform with an efficient way for searching for the optimal PI controller. Comparison study among fuzzy logic, neural network, Adaptive Neural Fuzzy Inference System , genetic algorithm, sequential quadratic programming and particle swarm optimization controllers are performed. These methods can improve the dynamic performance of the system in a better way.The PI-PSO controller is the best method based on integrated of time weight absolute error (ITAE)criteria which presented satisfactory performances and possesses good robustness (no overshoot, minimal rise time, steady state error almost to zero value). A comparison study has been done between selected methods and some other technique which showed that the proposed controller has setting time less than other methods by 40%.
Kevin Murphy D. Modeling and Estimation of Structural Damage Kevin Murphy D. Modeling and Estimation of Structural Damage Новинка

Kevin Murphy D. Modeling and Estimation of Structural Damage

9313.78 руб.
Modelling and Estimation of Damage in Structures is a comprehensiveguide to solving the type of modelling and estimation problems associated with the physics of structural damage. Provides a model-based approach to damage identification Presents an in-depth treatment of probability theory and random processes Covers both theory and algorithms for implementing maximum likelihood and Bayesian estimation approaches Includes experimental examples of all detection and identification approaches Provides a clear means by which acquired data can be used to make decisions regarding maintenance and usage of a structure
Chan Tze Fun Applied Intelligent Control of Induction Motor Drives Chan Tze Fun Applied Intelligent Control of Induction Motor Drives Новинка

Chan Tze Fun Applied Intelligent Control of Induction Motor Drives

13517.65 руб.
Induction motors are the most important workhorses in industry. They are mostly used as constant-speed drives when fed from a voltage source of fixed frequency. Advent of advanced power electronic converters and powerful digital signal processors, however, has made possible the development of high performance, adjustable speed AC motor drives. This book aims to explore new areas of induction motor control based on artificial intelligence (AI) techniques in order to make the controller less sensitive to parameter changes. Selected AI techniques are applied for different induction motor control strategies. The book presents a practical computer simulation model of the induction motor that could be used for studying various induction motor drive operations. The control strategies explored include expert-system-based acceleration control, hybrid-fuzzy/PI two-stage control, neural-network-based direct self control, and genetic algorithm based extended Kalman filter for rotor speed estimation. There are also chapters on neural-network-based parameter estimation, genetic-algorithm-based optimized random PWM strategy, and experimental investigations. A chapter is provided as a primer for readers to get started with simulation studies on various AI techniques. Presents major artificial intelligence techniques to induction motor drives Uses a practical simulation approach to get interested readers started on drive development Authored by experienced scientists with over 20 years of experience in the field Provides numerous examples and the latest research results Simulation programs available from the book's Companion Website This book will be invaluable to graduate students and research engineers who specialize in electric motor drives, electric vehicles, and electric ship propulsion. Graduate students in intelligent control, applied electric motion, and energy, as well as engineers in industrial electronics, automation, and electrical transportation, will also find this book helpful. Simulation materials available for download at www.wiley.com/go/chanmotor
Manjubala Bisi Artificial Neural Network Applications for Software Reliability Prediction Manjubala Bisi Artificial Neural Network Applications for Software Reliability Prediction Новинка

Manjubala Bisi Artificial Neural Network Applications for Software Reliability Prediction

15106.96 руб.
Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process is presented as well. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.
Kafayat Adeoye Fingerprint Recognition System Using Artifical Neural Network Kafayat Adeoye Fingerprint Recognition System Using Artifical Neural Network Новинка

Kafayat Adeoye Fingerprint Recognition System Using Artifical Neural Network

5252 руб.
Bachelor Thesis from the year 2017 in the subject Engineering - Computer Engineering, grade: First Class, University of Portsmouth, language: English, abstract: This project presents a fingerprint recognition system using neural network. To establish an objective assessment of the proposed neural network algorithm, fingerprint images from National institute of standards and technology (NIST) database were used. Image processing operations were carried out on the fingerprints prior to extracting the minutiae which are set as input into the network for verification or identification of a person. However, these processes are crucial to the performance of the neural network.Back-propagation neural network algorithm called Scaled Conjugate Gradient is used to train the network. The aim of this project is to implement a faster and reliable fingerprint minutiae matching algorithm and the Matlab experimental results show that the network has achieved an excellent performance in pattern recognition. Furthermore, the overall error rate is very minimal and the network generates 93.2% of accuracy for the fingerprint recognition system.
Moazzam Hossain Intrusion Detection with Artificial Neural Networks Moazzam Hossain Intrusion Detection with Artificial Neural Networks Новинка

Moazzam Hossain Intrusion Detection with Artificial Neural Networks

9877 руб.
Intrusion detection system is a detection mechanism that detects unauthorized, malicious presents in the computer systems. The purpose of this book is to design, implement and evaluate an anomaly based network intrusion detection system. The System learns about the normal users' behavior and finds the anomalies by matching with this normal behavior. A special type of neural network called backpropagation neural network is used for learning normal users' behavior. The network traffic that only contains information of normal users is presented with the neural network for learning about the normal users' behavior. The system performance has been tested by using a simulated computer network. The neural network is trained with huge,not so huge and small amount of data. The detection capability of the system has been tested with huge and small amount of data. It is seen from the performance analysis that the system performs well when trained with small amount of data. An overall detection rate of 98% has been achieved for both known and unknown attacks. Moreover, the system can detect 100% normal user.
Sevda Alaca Estimation of quantiles in a simulation model based on artificial neural networks Sevda Alaca Estimation of quantiles in a simulation model based on artificial neural networks Новинка

Sevda Alaca Estimation of quantiles in a simulation model based on artificial neural networks

5577 руб.
Master's Thesis from the year 2017 in the subject Mathematics - Stochastics, grade: 1,3, Technical University of Darmstadt, language: English, abstract: This thesis deals with the development of an "alpha"-quantile estimate based on a surrogate model with the use of artificial neuralnetworks. Using artificial neural networks as an estimate is considered a nonparametric approach.The estimation of a specific quantile of a data population is a widely used statistical task and a comprehensive way to discover the true relationship among variables. It can be classified as nonparametric regression, where it is one of the standard tasks. The most common selected levels for estimation are the first, second and third quartile (25, 50 and 75 percent). The quantile level is given by "alpha". A 25 percent quantile for example has 25 percent of the data distribution below the named quantile and 75 percent of the data distribution above it. Sometimes the tail regions of a population characteristic are of interest rather than the core of the distribution. Quantile estimation is applied in many different contexts - financial economics, survival analysis and environmental modelling areonly a few of them.
Cyril Goutte Statistical learning and regularisation for regression Cyril Goutte Statistical learning and regularisation for regression Новинка

Cyril Goutte Statistical learning and regularisation for regression

6927 руб.
Doctoral Thesis / Dissertation from the year 1997 in the subject Engineering - Artificial Intelligence, grade: n/a, , language: English, abstract: This thesis deals with the problem of statistical learning. By "learning"', we mean a process by which we obtain a model of a phenomenon using data measured on it. We focus on system identification and time series modelling, and our work is naturally slightly influenced by this concern. We will for example evoke only regression estimation, and no classification problem. However, most theoretical and practical aspects presented herein can easily be adapted. We study neural networks models. This research has traditionally been linked to computer science and artificial intelligence. In the last few years however, two distinct lines of thoughts seem to diverge: the first one stays close to the biological origins of the term - we will call it "neuro-biological"; the second considers neural networks as a model, and studies them from a statistical point of view. Our work concerns the second of these lines. Furthermore, we try to stay independent of any specific application, and keep a general approach to neural networks. We attempt to exhibit the links between neural networks and statistics, and show that neural computation can be naturally placed in the realm of traditional statistics. We thus compare neural networks to other models: linear regression as well as non-parametric estimators. We also consider some of the ...
Shah Raza Ali, Saengudomlert Poompat, Premanandana Rajatheva R. M. a. Zp-Nzp Ofdm Shah Raza Ali, Saengudomlert Poompat, Premanandana Rajatheva R. M. a. Zp-Nzp Ofdm Новинка

Shah Raza Ali, Saengudomlert Poompat, Premanandana Rajatheva R. M. a. Zp-Nzp Ofdm

8652 руб.
OFDM has been an attractive choice for digital modulation in the recent years due to its robustness to channel fading and invulnerability to intersymbol interference. However, to achieve an optimal performance the corresponding channel estimation scheme need to be accurate. This book focuses on the use of guard intervals between successive OFDM symbols for channel estimation. A hybrid of blind and semi-blind estimation techniques is proposed to create an energy efficient scheme that can maintain accurate channel estimation as well as a low transmission BER. Multipath fading channel are considered for performance evaluation. The proposed hybrid scheme is tested in static as well as time varying channel environments using computer simulations. Simulation results show that the proposed scheme outperforms the existing schemes on the basis of BER performance for the same SNR.
Kahsay Kiross Performance evaluation of channel estimation techniques for an LTE downlink system Kahsay Kiross Performance evaluation of channel estimation techniques for an LTE downlink system Новинка

Kahsay Kiross Performance evaluation of channel estimation techniques for an LTE downlink system

5477 руб.
Thesis (M.A.) from the year 2016 in the subject Engineering - Communication Technology, grade: 75%, Mekelle University, course: Communication engineering, language: English, abstract: In this thesis channel estimation techniques for LTE downlink named Least Square, Minimum Mean Square error and Maximum Likelihood estimation techniques are studied for the pilot symbol based channel estimation. In addition to this the performances of these three channel estimation techniques were also studied by introducing averaging, interpolation and hybrid methods. This work also investigates the complexity of the channel estimation techniques in terms of the number of complex multiplications and by varying the FFT size and number of CP. furthermore, the effect of varying the number of antennas at the transmitter and receiver ends, where 2 x 2 and 4 x 4 antenna arrangements are considered as a case studies. The performance of these channel estimation techniques is also studied for EVA standard channel model in LTE. The considered channel model is EVA standard channel model with Doppler shift of 300HZ.Simulation results in this thesis show that the ML channel estimation technique has the best performance. In terms of number of complex multiplications it is proved the ML has lower complexity. From the interpolating techniques it is shown the performance of the algorithm integrated with hybrid technique has the best performance. In addition to this it is shown that as the number of transmit and...
Xuefeng Yin Propagation Channel Characterization, Parameter Estimation, and Modeling for Wireless Communications Xuefeng Yin Propagation Channel Characterization, Parameter Estimation, and Modeling for Wireless Communications Новинка

Xuefeng Yin Propagation Channel Characterization, Parameter Estimation, and Modeling for Wireless Communications

10845.5 руб.
A comprehensive reference giving a thorough explanation of propagation mechanisms, channel characteristics results, measurement approaches and the modelling of channels Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular communication systems, RFID and ad hoc wireless communication systems. It gives a detailed introduction to aspects of channels before presenting the novel estimation and modelling techniques which can be used to achieve accurate models. To systematically guide readers through the topic, the book is organised in three distinct parts. The first part covers the fundamentals of the characterization of propagation channels, including the conventional single-input single-output (SISO) propagation channel characterization as well as its extension to multiple-input multiple-output (MIMO) cases. Part two focuses on channel measurements and channel data post-processing. Wideband channel measurements are introduced, including the equipment, technology and advantages and disadvantages of different data acquisition schemes. The channel parameter estimation methods are then presented, which include conventional spectral-based estimation, the specular-path-model based high-resolution method, and the newly derived power spectrum estimation methods. Measurement results are used to compare the performance of the different estimation methods. The third part gives a complete introduction to different modelling approaches. Among them, both scattering theoretical channel modelling and measurement-based channel modelling approaches are detailed. This part also approaches how to utilize these two modelling approaches to investigate wireless channels for conventional cellular systems and some new emerging communication systems. This three-part approach means the book caters for the requirements of the audiences at different levels, including readers needing introductory knowledge, engineers who are looking for more advanced understanding, and expert researchers in wireless system design as a reference. Presents technical explanations, illustrated with examples of the theory in practice Discusses results applied to 4G communication systems and other emerging communication systems, such as relay, CoMP, and vehicle-to-vehicle rapid time-variant channels Can be used as comprehensive tutorial for students or a complete reference for engineers in industry Includes selected illustrations in color Program downloads available for readers Companion website with program downloads for readers and presentation slides and solution manual for instructors Essential reading for Graduate students and researchers interested in the characteristics of propagation channel, or who work in areas related to physical layer architectures, air interfaces, navigation, and wireless sensing
Biao Huang Dynamic Modeling and Predictive Control in Solid Oxide Fuel Cells. First Principle and Data-based Approaches Biao Huang Dynamic Modeling and Predictive Control in Solid Oxide Fuel Cells. First Principle and Data-based Approaches Новинка

Biao Huang Dynamic Modeling and Predictive Control in Solid Oxide Fuel Cells. First Principle and Data-based Approaches

12391.18 руб.
The high temperature solid oxide fuel cell (SOFC) is identified as one of the leading fuel cell technology contenders to capture the energy market in years to come. However, in order to operate as an efficient energy generating system, the SOFC requires an appropriate control system which in turn requires a detailed modelling of process dynamics. Introducting state-of-the-art dynamic modelling, estimation, and control of SOFC systems, this book presents original modelling methods and brand new results as developed by the authors. With comprehensive coverage and bringing together many aspects of SOFC technology, it considers dynamic modelling through first-principles and data-based approaches, and considers all aspects of control, including modelling, system identification, state estimation, conventional and advanced control. Key features: Discusses both planar and tubular SOFC, and detailed and simplified dynamic modelling for SOFC Systematically describes single model and distributed models from cell level to system level Provides parameters for all models developed for easy reference and reproducing of the results All theories are illustrated through vivid fuel cell application examples, such as state-of-the-art unscented Kalman filter, model predictive control, and system identification techniques to SOFC systems The tutorial approach makes it perfect for learning the fundamentals of chemical engineering, system identification, state estimation and process control. It is suitable for graduate students in chemical, mechanical, power, and electrical engineering, especially those in process control, process systems engineering, control systems, or fuel cells. It will also aid researchers who need a reminder of the basics as well as an overview of current techniques in the dynamic modelling and control of SOFC.
Yaligar Ravindra, Isaac R.K., Madolli Mallappa Modeling Land Use Change and Runoff for Chaka Block, Uttarapradesh Yaligar Ravindra, Isaac R.K., Madolli Mallappa Modeling Land Use Change and Runoff for Chaka Block, Uttarapradesh Новинка

Yaligar Ravindra, Isaac R.K., Madolli Mallappa Modeling Land Use Change and Runoff for Chaka Block, Uttarapradesh

5214 руб.
This study aims to determine land use change and runoff using the USDA Soil Conservation Service curve number (SCS-CN) method for Chaka block, located at 24o 47'00"N to 25o 47'00"N North latitude and 81o 19'00"E to 82o 21'00"E longitude, situated at Allahabad district, Uttar Pradesh .Antecedent moisture content (AMC) was calculated by taking preceding five days rainfall which gave three conditions AMC I, AMC II and AMC III. Weighted Curve Number for the entire selected micro-watershed was calculated based on site information of the block and found to be 78 for AMC II. The CN values corresponding to AMC I and AMC III were 59.82 and 89.08 respectively. The runoff for each storm events was estimated following Curve Number method and it is found that among the selected storm events maximum rainfall of 203 mm occurred in September 1, 2000 generating 169.89 mm of runoff. Runoff volume of the micro-watershed for each storm events were also calculated and maximum runoff is found is 26.09 million m³. This value will be useful for design of soil and water conservation structures in block. The simulation of runoff events it is observed that the value of estimated runoff and simulated runoff
Tai Doan Convolutional Neural Network in classifying scanned documents Tai Doan Convolutional Neural Network in classifying scanned documents Новинка

Tai Doan Convolutional Neural Network in classifying scanned documents

1852 руб.
Internship Report from the year 2016 in the subject Computer Science - Applied, University of Science and Technology of Hanoi, course: Internship, language: English, abstract: In this project, I created and augmented a dataset from a number of given images to train and test convolutional neural network which is used to classify five classes of images of scanned documents. In order to generate the dataset, some image processing techniques were applied such as sliding-window, rotating, flipping and pyramid-sizing. The result of this phase is a set of images having same size 244x224x3. These images after being labeled were divided into three dataset for training, validating and testing the network. The network is a simple convolution neural network which is also called LeNet. It has three convolutional layers and one fully connected layer. After being trained and validated, the best state of the network was pointed out and tested on the testing dataset and some real images. The result showed that the LeNet was able to classify images of documents in a pretty high accuracy. At the end of the project, I modified the network and discussed the affect that those changes had on the network with the purpose of creating another similar network which can perform better than the original one. The result proved that it worked a little better than its original version.
Mahmoud Jazzar Computer Network Intrusion Detection Mahmoud Jazzar Computer Network Intrusion Detection Новинка

Mahmoud Jazzar Computer Network Intrusion Detection

6190 руб.
A typical problem that arises when deploying intrusion detection sensors is their affinities of producing high rate of false alerts. Thus, it needs huge analysis efforts and time consuming odd jobs at higher levels. In this study, we have investigated an approach to anomaly intrusion detection based on causal knowledge reasoning. The approach is anomaly-based and utilizes causal knowledge inference based fuzzy cognitive maps (FCM) and self organizing maps (SOM). A set of parallel neural network classifiers (SOM) are used to do an initial recognition of the network traffic flow to detect abnormal behaviors. The FCM is incorporated to eliminate ambiguities of odd neurons and making final decisions. Based on the domain knowledge of network data the SOM/FCM combination presents quantitative and qualitative matching correspondences which in turn reduce the number of suspicious neurons i.e. reduce the number of false alerts. This method works as a unique fuzzy clustering approach and we have demonstrated its performance using DARPA 1999 network traffic data set. The method has also the flexibility of features selection for further exploration.
Lih Chieh Png Morphological Shared-Weight Neural Network for Face Recognition Lih Chieh Png Morphological Shared-Weight Neural Network for Face Recognition Новинка

Lih Chieh Png Morphological Shared-Weight Neural Network for Face Recognition

4178 руб.
An algorithm based on morphological shared-weight neural network is introduced. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face. For evaluation, we test for robustness under variations in gray levels and noise while varying the network’s configuration to optimize recognition efficiency and processing time. Results show that the MSNN performs better for grayscale image pattern classification than ordinary neural networks.
Asadullah Attal Develop of Neural Network Models to Predict Construction Cost/Duration Asadullah Attal Develop of Neural Network Models to Predict Construction Cost/Duration Новинка

Asadullah Attal Develop of Neural Network Models to Predict Construction Cost/Duration

5224 руб.
This Publication Contains the Analysis of Prediction of the Future highways Cost and Construction Duration based on the Artificial Neural Network Regression analysis. More than 1500 construction projects data was retrieved and more than 400 projects data was classified as the most important data for this analysis. The most effective factors were also identified by this regression analysis. As a result of this analysis, the outcome was more optimistic than our prediction.
Stefan Vogt A Design and Development Method for Artificial Neural Network Projects Stefan Vogt A Design and Development Method for Artificial Neural Network Projects Новинка

Stefan Vogt A Design and Development Method for Artificial Neural Network Projects

4639 руб.
Inhaltsangabe:Abstract: In the 1980s research efforts and successes made artificial neural networks popular. Since the 1990s engineers have been using this foundation for problem solving. But artifiial neural network solutions for "real-world" problems are sometimes hard to find because of the complexity of the domain and because of the vast number of design attributes the engineer has to deal with. This thesis provides a structured overview of attributes in the design process of artificial neural networks and reviews technical process models. Current development methods for artificial neural networks are then reviewed and critiqued. The thesis concludes with a new design and development method for artificial neural networks. Inhaltsverzeichnis:Table of Contents: List of figuresx List of tablesxi Introduction1 1.Design attributes in ANN3 1.1ANN models4 1.1.1Node level7 1.1.2Network level9 1.1.3Training level9 1.2Data and data representation10 1.3Global system design12 1.4Hardware and software implementation13 1.5Characteristics of ANNs15 1.5.1Advantages of ANNs15 1.5.2Limitations and concerns16 2.Technical process models and engineering methods18 2.1Why use an engineering method?18 2.2Evolutionary model of engineering discipline20 2.3Overview of technical process models22 2.3.1Taxonomy of technical process models24 2.3.2Prototyping25 2.3.3Incremental method26 2.3.4Strict contractual approach26 2.3.5Deciding on process models and methods26 2.3.6Examples of process mode...
Dutt Sunderiyal Vinita, Kumar Singh Yadav Ajit Handwritten Character Recognition Using Artificial Neural Network Dutt Sunderiyal Vinita, Kumar Singh Yadav Ajit Handwritten Character Recognition Using Artificial Neural Network Новинка

Dutt Sunderiyal Vinita, Kumar Singh Yadav Ajit Handwritten Character Recognition Using Artificial Neural Network

9702 руб.
A Neural network is a machine that is designed to model the way in which the brain performs a particular task or function of interest: The network is usually implemented by using electronic components or is simulated in software on a digital computer. " A neural network is a massively parallel distributed processor made up of simple processing units which has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: 1)Knowledge is required by the network from its environment through a learning process. 2)Inter neuron connection strengths, known as synaptic weights, are used to store the acquired knowledge". In this book, we proposed a system capable of recognizing handwritten characters or symbols, Our aim is to build a system for handwritten character recognition. The system should be such that it should be able to handle transformation of scaling, translation or a combination of both. The objective is to bring out accurate results even for images with noise in them.
Elena-Niculina Drăgoi,Silvia Curteanu and Vlad Dafinescu Modelling and optimization of chemical engineering processes Elena-Niculina Drăgoi,Silvia Curteanu and Vlad Dafinescu Modelling and optimization of chemical engineering processes Новинка

Elena-Niculina Drăgoi,Silvia Curteanu and Vlad Dafinescu Modelling and optimization of chemical engineering processes

7075 руб.
In the latest years, the use of computational techniques for solving different problems is rising. In this context, engineers are faced with the difficult choice of selecting and applying the best methods suited for modelling and optimization purposes. On the other hand, there are cases when the conventional approaches are not feasible and alternative methods must be developed. This book is aimed to help engineers in the application and implementation of neural networks and bio-inspired algorithms for solving day-to-day engineering problems and specific process optimization. New techniques based on artificial neural networks, differential evolution algorithm, and artificial immune systems are presented. They have proven to be flexible and efficient, having as main advantages the generalization capability and the opportunity of providing useful information for experimental practice.
Mohammad Arashi Theory of Ridge Regression Estimation with Applications Mohammad Arashi Theory of Ridge Regression Estimation with Applications Новинка

Mohammad Arashi Theory of Ridge Regression Estimation with Applications

12404.43 руб.
A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators.The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.
Akira Hirose Complex-Valued Neural Networks. Advances and Applications Akira Hirose Complex-Valued Neural Networks. Advances and Applications Новинка

Akira Hirose Complex-Valued Neural Networks. Advances and Applications

10575.57 руб.
Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains. Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of: Conventional complex-valued neural networks Quaternionic neural networks Clifford-algebraic neural networks Presented by international experts in the field, Complex-Valued Neural Networks: Advances and Applications is ideal for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians interested in computational intelligence, artificial intelligence, machine learning theories, and algorithms.
Jan Jantzen Foundations of Fuzzy Control. A Practical Approach Jan Jantzen Foundations of Fuzzy Control. A Practical Approach Новинка

Jan Jantzen Foundations of Fuzzy Control. A Practical Approach

10303.08 руб.
Foundations of Fuzzy Control: A Practical Approach, 2nd Edition has been significantly revised and updated, with two new chapters on Gain Scheduling Control and Neurofuzzy Modelling. It focuses on the PID (Proportional, Integral, Derivative) type controller which is the most widely used in industry and systematically analyses several fuzzy PID control systems and adaptive control mechanisms. This new edition covers the basics of fuzzy control and builds a solid foundation for the design of fuzzy controllers, by creating links to established linear and nonlinear control theory. Advanced topics are also introduced and in particular, common sense geometry is emphasised. Key features Sets out practical worked through problems, examples and case studies to illustrate each type of control system Accompanied by a website hosting downloadable MATLAB programs Accompanied by an online course on Fuzzy Control which is taught by the author. Students can access further material and enrol at the companion website Foundations of Fuzzy Control: A Practical Approach, 2nd Edition is an invaluable resource for researchers, practitioners, and students in engineering. It is especially relevant for engineers working with automatic control of mechanical, electrical, or chemical systems.
Md. Safiur Rahman Mahdi, A.B.M. Zunaid Haque, S.M. Al Mamun Protein Secondary Structure Prediction Md. Safiur Rahman Mahdi, A.B.M. Zunaid Haque, S.M. Al Mamun Protein Secondary Structure Prediction Новинка

Md. Safiur Rahman Mahdi, A.B.M. Zunaid Haque, S.M. Al Mamun Protein Secondary Structure Prediction

8377 руб.
Protein secondary structure prediction is a very hot topic in bioinformatics. Predicting protein secondary structure means to find out the portions that contain Helix and Sheet in protein sequence. There are several methods for predicting protein secondary structure. The methods like Genetic Algorithm, Hidden Markov Model and different kinds of Neural Networks are there. Genetic Algorithm mostly deals with protein tertiary structure and sequence alignment, for Hidden Markov Model the accuracy is not good and Neural Network is the most successful for predicting protein secondary structure. So, we used the method named "Feed Forward Neural Network" and implemented it with JOONE (Java Object Oriented Neural Engine) editor. At first we have classified the 20 protein according to their structure, size and hydrophobic manner. Then we have modeled a new architecture in feed forward network and used those classified proteins as input. Our achieved accuracy of helix prediction is 71% and sheet prediction is 65%. The result shows the improvement over previous works done in this regard. We hope that our work will be a future directive in this arena.
Suriani Ibrahim and Mohd Rafie Johan Conductivity Studies And Neural Networks Model Suriani Ibrahim and Mohd Rafie Johan Conductivity Studies And Neural Networks Model Новинка

Suriani Ibrahim and Mohd Rafie Johan Conductivity Studies And Neural Networks Model

2890 руб.
In the experiment, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF6), ethylene carbonate (EC) and carbon nanotube (CNT) were mixed ad various ratios. . The conductivity increases from 10-10 to 10-5 Scm-1 upon the addition of salt. The incorporation of EC and αCNTs to the salted polymer enhances the conductivity significantly to 10-4 and 10-3 Scm-1. In neural network training, different chemical composition and real impedance were used as inputs and imaginary impedance in the produced polymer electrolytes was used as outputs. After training process, the test data were used to check system accuracy. As a result, the neural network was found successful for the prediction of imaginary impedance of nanocomposite polymer electrolyte system.
B. B. Misra, P. K. Dash, G. Panda Neuro-Swarm Techniques for Classifier Design B. B. Misra, P. K. Dash, G. Panda Neuro-Swarm Techniques for Classifier Design Новинка

B. B. Misra, P. K. Dash, G. Panda Neuro-Swarm Techniques for Classifier Design

9814 руб.
Since last few decades, the wide use of advanced technologies made massive growth in the availability of data from different resources, which lead to the challenge of extracting useful knowledge for specific applications. Classification is one of the most important tasks in mining knowledge from such resources. Though a wide number of traditional techniques are available for classification, but finding a high performance robust classification method, applicable to different real world problem is difficult. This book provides a set of techniques for classification which has been applied to problems from multiple disciplines such as life science, physical science, social science, business, and games. Design of a wide range of classifiers using Polynomial Neural Network and its hybridization with Particle Swam Optimization, Fuzzy logic, Artificial Neural Network has been presented in this book with their scope and limitations. The algorithms and models presented for the large range of classifiers will certainly help the readers in using it conveniently for their application areas.
Gholam Hossein Roshani,Farzin Shama and Seyed Amir Hossein Feghhi Some Applications of Artificial Neural Network in Nuclear Engineering Gholam Hossein Roshani,Farzin Shama and Seyed Amir Hossein Feghhi Some Applications of Artificial Neural Network in Nuclear Engineering Новинка

Gholam Hossein Roshani,Farzin Shama and Seyed Amir Hossein Feghhi Some Applications of Artificial Neural Network in Nuclear Engineering

3212 руб.
In this book some applications of artificial neural network in nuclear engineering are presented. In densitometry, number of scattered and counted gamma photons highly depends on material density. Using this relation, two different multi-layer perceptron artificial neural networks are proposed to predict material density. The results of proposed ANNs show that the presented model could be employed in densitometry of materials. Furthermore, the development of an ANN model for prediction of the highest value of X-ray yield in PFs is showed. The comparison between predicted and experimental results by ANN model illustrates that there is a good adaptation between them. So, the MLP architecture can be applied as a high efficient tool to predict the highest value of X-ray yield in the PFs.
Bangjun Lei Classification, Parameter Estimation and State Estimation. An Engineering Approach Using MATLAB Bangjun Lei Classification, Parameter Estimation and State Estimation. An Engineering Approach Using MATLAB Новинка

Bangjun Lei Classification, Parameter Estimation and State Estimation. An Engineering Approach Using MATLAB

10071.55 руб.
A practical introduction to intelligent computer vision theory, design, implementation, and technology The past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods—especially among adaboost varieties and particle filtering methods—have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post-graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including: PRTools5 software for MATLAB—especially the latest representation and generalization software toolbox for PRTools5 Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods All new coverage of the Adaboost and its implementation in PRTools5. A valuable working resource for professionals and an excellent introduction for advanced-level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website.
Gasser Eric Hydrological Modelling with Tree-Ring Data - A Feasibility Study Gasser Eric Hydrological Modelling with Tree-Ring Data - A Feasibility Study Новинка

Gasser Eric Hydrological Modelling with Tree-Ring Data - A Feasibility Study

5214 руб.
Seasonal precipitation and temperature reconstructions based on tree-ring chronologies were implemented into the hydrological modelling system PREVAH (PREcipitation-Runoff-EVApotranspiration-HRU model) to model past annual and monthly hydrological cycles back to 500 B.C. Two Swiss catchments were selected for which the hydrological modelling System PREVAH has been successfully calibrated: The Murg stream in the canton of Thurgau and the Dischmabach stream in the canton of Graubuenden. Unfortunately, due to an internal model error, false volume corrections were computed by PREVAH. Therefore, no statistical tests regarding absolute values were conducted. However, correlation coefficients were calculated and for now, this first attempt does detect paleohydroclimatic trends. Possible error sources are also examined and discussed thus the results depend on the tree-ring quality.
Michael Doumpos Multicriteria Decision Aid and Artificial Intelligence. Links, Theory and Applications Michael Doumpos Multicriteria Decision Aid and Artificial Intelligence. Links, Theory and Applications Новинка

Michael Doumpos Multicriteria Decision Aid and Artificial Intelligence. Links, Theory and Applications

9063.88 руб.
Presents recent advances in both models and systems for intelligent decision making. Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems. The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering. Multicriteria Decision Aid and Artificial Intelligence: Covers all of the recent advances in intelligent decision making. Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems. Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications. Is written by experts in the field. This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial.
Eddy van der Maarel Modelling Transport Eddy van der Maarel Modelling Transport Новинка

Eddy van der Maarel Modelling Transport

6997.37 руб.
Already the market leader in the field, Modelling Transport has become still more indispensible following a thorough and detailed update. Enhancements include two entirely new chapters on modelling for private sector projects and on activity-based modelling; a new section on dynamic assignment and micro-simulation; and sizeable updates to sections on disaggregate modelling and stated preference design and analysis. It also tackles topical issues such as valuation of externalities and the role of GPS in travel time surveys. Providing unrivalled depth and breadth of coverage, each topic is approached as a modelling exercise with discussion of the roles of theory, data, model specification, estimation, validation and application. The authors present the state of the art and its practical application in a pedagogic manner, easily understandable to both students and practitioners. Follows on from the highly successful third edition universally acknowledged as the leading text on transport modelling techniques and applications Includes two new chapters on modelling for private sector projects and activity based modeling, and numerous updates to existing chapters Incorporates treatment of recent issues and concerns like risk analysis and the dynamic interaction between land use and transport Provides comprehensive and rigorous information and guidance, enabling readers to make practical use of every available technique Relates the topics to new external factors and technologies such as global warming, valuation of externalities and global positioning systems (GPS).
Sat Kumar Tomer Soil Moisture Modelling and Assimilation Sat Kumar Tomer Soil Moisture Modelling and Assimilation Новинка

Sat Kumar Tomer Soil Moisture Modelling and Assimilation

8502 руб.
The book explains the methodology of soil moisture modelling and assimilation. The book begins with the modelling of soil moisture using the numerical solution of physical based equations. The book explains how can the measurements be used in improving the state of the model, hence in improving the predictions by means of data assimilation. The linear Kalman, Extended Kalman and Ensemble Kalman filtering approaches are given for data assimilation. The approaches to measure the soil moisture in field, and the way to analyse the field data by means of the variogram analysis are also explained. The parameter estimation using inverse modelling is also covered in the book.
Sharma Krishankant, Vishwakarma Sachin, Laharia Abhishek Ultra Wide Band Band Pass Filter Sharma Krishankant, Vishwakarma Sachin, Laharia Abhishek Ultra Wide Band Band Pass Filter Новинка

Sharma Krishankant, Vishwakarma Sachin, Laharia Abhishek Ultra Wide Band Band Pass Filter

8927 руб.
This book provide the Detailed Knowledge of the design of Artificial Neural Network (ANN) based PID controller, to realize fast governor action in a power generation plant. The design technique is applied to single area, two area systems, to tune the parameters of the PID controller. Feed forward neural network architecture is chosen for the design of controller, which is trained by a popular back propagation algorithm. Performance of the proposed ANN based Controller, is compared with conventional integral and PID controllers, through dynamic simulation. It is observed that ANN based controller provides better performance. The frame work for interactive learning presented in this Book differs from this used in more established field of interactive evolutionary computation. The main difference is requirement of expert knowledge from neural network area, which makes it a research tool. We hope in future interactive learning of neural networks can be defined enough to be used in real world applications.
Filo Orna Information Processing by Biochemical Systems. Neural Network-Type Configurations Filo Orna Information Processing by Biochemical Systems. Neural Network-Type Configurations Новинка

Filo Orna Information Processing by Biochemical Systems. Neural Network-Type Configurations

6834.37 руб.
A Research-Driven Resource on Building Biochemical Systems to Perform Information Processing Functions Information Processing by Biochemical Systems describes fully delineated biochemical systems, organized as neural network–type assemblies. It explains the relationship between these two apparently unrelated fields, revealing how biochemical systems have the advantage of using the «language» of the physiological processes and, therefore, can be organized into the neural network–type assemblies, much in the way that natural biosystems are. A wealth of information is included concerning both the experimental aspects (such as materials and equipment used) and the computational procedures involved. This authoritative reference: Addresses network-type connectivity, considered to be a key feature underlying the information processing ability of the brain Describes novel scientific achievements, and serves as an aid for those interested in further developing biochemical systems that will perform information-processing functions Provides a viable approach for furthering progress in the area of molecular electronics and biocomputing Includes results obtained in experimental studies involving a variety of real enzyme systems Information Processing by Biochemical Systems is intended for graduate students and professionals, as well as biotechnologists.
Krishankant Sharma,Sachin Vishwakarma and Abhishek Laharia Ultra Wide Band Band Pass Filter Krishankant Sharma,Sachin Vishwakarma and Abhishek Laharia Ultra Wide Band Band Pass Filter Новинка

Krishankant Sharma,Sachin Vishwakarma and Abhishek Laharia Ultra Wide Band Band Pass Filter

3212 руб.
This book provide the Detailed Knowledge of the design of Artificial Neural Network (ANN) based PID controller, to realize fast governor action in a power generation plant. The design technique is applied to single area, two area systems, to tune the parameters of the PID controller. Feed forward neural network architecture is chosen for the design of controller, which is trained by a popular back propagation algorithm. Performance of the proposed ANN based Controller, is compared with conventional integral and PID controllers, through dynamic simulation. It is observed that ANN based controller provides better performance. The frame work for interactive learning presented in this Book differs from this used in more established field of interactive evolutionary computation. The main difference is requirement of expert knowledge from neural network area, which makes it a research tool. We hope in future interactive learning of neural networks can be defined enough to be used in real world applications.
Kyurkchiev Nikolay, Markov Svetoslav Sigmoid Functions. Some Approximation and Modelling Aspects Kyurkchiev Nikolay, Markov Svetoslav Sigmoid Functions. Some Approximation and Modelling Aspects Новинка

Kyurkchiev Nikolay, Markov Svetoslav Sigmoid Functions. Some Approximation and Modelling Aspects

4789 руб.
The subject of this book is cross-disciplinary. Sigmoid functions present a field of interest both for fundamental as well as application-driven research. We have tried to give the readers the flavour of both perspectives. From the perspective of fundamental science sigmoid functions are of special interest in abstract areas such as approximation theory and probability theory. More specifically, sigmoid function are an object of interest in Hausdorff approximations, fuzzy set theory, cumulative distribution functions, etc. From the perspective of applied mathematics and modelling sigmoid functions find their place in numerous areas of life and social sciences, physics and engineering, to mention a few familiar applications: population dynamics, artificial neural networks, signal and image processing. We consider this book to be suitable for undergraduate students attending master programs from applied mathematics and mathematical modelling. Readers with some computational programming skills may find numerous ideas for writing their own programs to run graphics of various sigmoid functions (the ones presented in the book make use of the computer algebra system Mathematica).
Arthur Dexter L. Monitoring and Control of Information-Poor Systems. An Approach based on Fuzzy Relational Models Arthur Dexter L. Monitoring and Control of Information-Poor Systems. An Approach based on Fuzzy Relational Models Новинка

Arthur Dexter L. Monitoring and Control of Information-Poor Systems. An Approach based on Fuzzy Relational Models

12007.52 руб.
The monitoring and control of a system whose behaviour is highly uncertain is an important and challenging practical problem. Methods of solution based on fuzzy techniques have generated considerable interest, but very little of the existing literature considers explicit ways of taking uncertainties into account. This book describes an approach to the monitoring and control of information-poor systems that is based on fuzzy relational models which generate fuzzy outputs. The first part of Monitoring and Control of Information-Poor Systems aims to clarify why design decisions must take account of the uncertainty associated with optimal choices, and to explain how a fuzzy relational model can be used to generate a fuzzy output, which reflects the uncertainties associated with its predictions. Part two gives a brief introduction to fuzzy decision-making and shows how it can be used to design a predictive control scheme that is suitable for controlling information-poor systems using inaccurate measurements. Part three describes different ways in which fuzzy relational models can be generated online and explains the practical issues associated with their identification and application. The final part of the book provides examples of the use of the previously described techniques in real applications. Key features: Describes techniques applicable to a wide range of engineering, environmental, medical, financial and economic applications Uses simple examples to help explain the basic techniques for dealing with uncertainty Describes a novel design approach based on the use of fuzzy relational models Considers practical issues associated with applying the techniques to real systems Monitoring and Control of Information-Poor Systems forms an invaluable resource for a wide range of graduate students, and is also a comprehensive reference for researchers and practitioners working on problems involving mathematical modelling and control.
Antonin Dvorak Insight into Fuzzy Modeling Antonin Dvorak Insight into Fuzzy Modeling Новинка

Antonin Dvorak Insight into Fuzzy Modeling

9296.14 руб.
Provides a unique and methodologically consistent treatment of various areas of fuzzy modeling and includes the results of mathematical fuzzy logic and linguistics This book is the result of almost thirty years of research on fuzzy modeling. It provides a unique view of both the theory and various types of applications. The book is divided into two parts. The first part contains an extensive presentation of the theory of fuzzy modeling. The second part presents selected applications in three important areas: control and decision-making, image processing, and time series analysis and forecasting. The authors address the consistent and appropriate treatment of the notions of fuzzy sets and fuzzy logic and their applications. They provide two complementary views of the methodology, which is based on fuzzy IF-THEN rules. The first, more traditional method involves fuzzy approximation and the theory of fuzzy relations. The second method is based on a combination of formal fuzzy logic and linguistics. A very important topic covered for the first time in book form is the fuzzy transform (F-transform). Applications of this theory are described in separate chapters and include image processing and time series analysis and forecasting. All of the mentioned components make this book of interest to students and researchers of fuzzy modeling as well as to practitioners in industry. Features: Provides a foundation of fuzzy modeling and proposes a thorough description of fuzzy modeling methodology Emphasizes fuzzy modeling based on results in linguistics and formal logic Includes chapters on natural language and approximate reasoning, fuzzy control and fuzzy decision-making, and image processing using the F-transform Discusses fuzzy IF-THEN rules for approximating functions, fuzzy cluster analysis, and time series forecasting Insight into Fuzzy Modeling is a reference for researchers in the fields of soft computing and fuzzy logic as well as undergraduate, master and Ph.D. students. Vilém Novák, D.Sc. is Full Professor and Director of the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic. Irina Perfilieva, Ph.D. is Full Professor, Senior Scientist, and Head of the Department of Theoretical Research at the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic. Antonín Dvorák, Ph.D. is Associate Professor, and Senior Scientist at the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic.
Navjot Kaur Neural Stem Cell Assays Navjot Kaur Neural Stem Cell Assays Новинка

Navjot Kaur Neural Stem Cell Assays

12003.84 руб.
Neural stem cells offer a valuable model system for delineating the cellular and developmental processes in normal and diseased states of the central nervous system. In particular, neural stem cells have huge potential in regenerative medicine, owing to their expansion capability in culture and the ability to differentiate into multiple sub-neural lineages. Neural Stem Cell Assays provides a detailed and comprehensive review of the basic methods for neural stem cell cultures. Including an overview of progress in the field over the past decade, Neural Stem Cell Assays is a one-stop reference for consistent methods and reliable tools that span the entire assay work flow, from isolation or generation of neural stem cells to characterization, manipulation and final application of neural stem cells in disease paradigms such as Parkinson's disease, multiple sclerosis and amyotrophic lateral sclerosis. An excellent source of information for academic, pharmaceutical and biotechnology researchers who are new to the neural stem cell field, Neural Stem Cell Assays is an invaluable to experienced users who wish to integrate newly developed tools and technologies into their workflow. The book also covers important course material for students at the undergraduate and graduate level who are learning the basics of neural stem cell cultures, and differentiation to sub-neural lineages.
William F. Allman Apprentices of Wonder. Inside the Neural Network Revolution William F. Allman Apprentices of Wonder. Inside the Neural Network Revolution Новинка

William F. Allman Apprentices of Wonder. Inside the Neural Network Revolution

1177 руб.
In the vein of The Soul of a New Machine, a dramatic chronicle of a new revolution in brain-mind science comes this accessible book on the scientists who are creating startling new theories of how the mind works as the forge a new kind of artificial intelligence called neural networks--or, the first thinking machines
Reinhard Viertl Statistical Methods for Fuzzy Data Reinhard Viertl Statistical Methods for Fuzzy Data Новинка

Reinhard Viertl Statistical Methods for Fuzzy Data

9219.83 руб.
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy measurement results. Furthermore, statistical methods are then generalized to the analysis of fuzzy data and fuzzy a-priori information. Key Features: Provides basic methods for the mathematical description of fuzzy data, as well as statistical methods that can be used to analyze fuzzy data. Describes methods of increasing importance with applications in areas such as environmental statistics and social science. Complements the theory with exercises and solutions and is illustrated throughout with diagrams and examples. Explores areas such quantitative description of data uncertainty and mathematical description of fuzzy data. This work is aimed at statisticians working with fuzzy logic, engineering statisticians, finance researchers, and environmental statisticians. It is written for readers who are familiar with elementary stochastic models and basic statistical methods.
Mrutyunjaya Sahu Prediction of Flow and its Resistance in Compound Open Channels Mrutyunjaya Sahu Prediction of Flow and its Resistance in Compound Open Channels Новинка

Mrutyunjaya Sahu Prediction of Flow and its Resistance in Compound Open Channels

8514 руб.
This monograph analyses the intricacies behind the turbulent flow in compound open channels. Mainly, due to velocity difference in main channel and flood plains large shear layer produces, which provokes uncertainty in measuring discharge for compound channel flow conditions. In this study an effort has been made by Large eddy simulation (LES) to analyse this complected situation precisely. Further, assessment have been carried out for prediction of discharge and its resistance factor by adaptable machine learning algorithms such as artificial neural network and artificial neuro-fuzzy inference system (ANFIS) for different hydraulic conditions.
Juha Salmelin LTE Backhaul. Planning and Optimization Juha Salmelin LTE Backhaul. Planning and Optimization Новинка

Juha Salmelin LTE Backhaul. Planning and Optimization

8308.32 руб.
The aim of this book is to enable network planners to realize and maintain cost efficient LTE backhaul networks, which meet the necessary performance requirements. Through an introduction to the technology background, the economical modelling, the dimensioning theory, planning and optimization processes and relevant network management aspects, the reader shall obtain all relevant information to achieve good backhaul results in their own network environment. It is aimed at network planners and other experts with responsibilities for LTE IP network dimensioning, LTE network planning, providing and managing leased lines, business management, LTE IP network operation and optimization.
Gill Amandeep, Kaur Manbir, Singh Nirbhowjap Speed Control of Brushless DC Motor by Neural Network Pid Controller Gill Amandeep, Kaur Manbir, Singh Nirbhowjap Speed Control of Brushless DC Motor by Neural Network Pid Controller Новинка

Gill Amandeep, Kaur Manbir, Singh Nirbhowjap Speed Control of Brushless DC Motor by Neural Network Pid Controller

8789 руб.
The aim of the book is to design a simulation model of Brushless dc motor and to control its speed at different values of load torques.In this light, new control schemes should be devised for a better solution of a non linear system. Recently, work has been started toward the development of Artificial Neural Network (ANN) based intelligent controllers. The ANN has several key features that make it highly suitable for BLDCM speed applications. The ANN based PID controller is used for the speed control of BLDCM at different values of load torque and its comparison is done with the conventional controllers like PID and PI controllers.
Paolo Santi Mobility Models for Next Generation Wireless Networks. Ad Hoc, Vehicular and Mesh Networks Paolo Santi Mobility Models for Next Generation Wireless Networks. Ad Hoc, Vehicular and Mesh Networks Новинка

Paolo Santi Mobility Models for Next Generation Wireless Networks. Ad Hoc, Vehicular and Mesh Networks

10575.57 руб.
Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networks Offers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and opportunistic networks Demonstrates the practices for designing effective protocol/applications for next generation wireless networks Includes case studies showcasing the importance of properly understanding fundamental mobility model properties in wireless network performance evaluation
C. Baburaj Statistical Estimation C. Baburaj Statistical Estimation Новинка

C. Baburaj Statistical Estimation

9189 руб.
Statistical inference has two parts; estimation and testing of hypothesis. Here in this book, estimation is discussed and importance is given to point estimation. The properties of point estimators are discussed in detail. The commonly used methods of estimation are also discussed and in the last chapter, Bayesian estimation methods is discussed. The book will be useful to the under graduate students in Statistics and for all those who is studying the subject Statistics and doing Statistical analysis.
Saraswathi Muthusamy Fuzzy B-double star Opensets and fuzzy chi-double star Closed Sets Saraswathi Muthusamy Fuzzy B-double star Opensets and fuzzy chi-double star Closed Sets Новинка

Saraswathi Muthusamy Fuzzy B-double star Opensets and fuzzy chi-double star Closed Sets

3212 руб.
The concept of fuzzy set was introduced by Zadeh in his classical paper (Zadeh, 1965). Balasubramaniam and Sundaram defined generalized fuzzy closed sets in a fuzzy topological space and introduced certain types of fuzzy continuous functions between fuzzy topological spaces.(X, T) and (Y, S) denotes a fuzzy topological spaces.Also in this book I introduced different types of open sets and closed sets in fuzzy topological spaces.I think it will be very useful to co higher research in this field.
Asif Nawaz Neuro Fuzzy Information Retrieval Asif Nawaz Neuro Fuzzy Information Retrieval Новинка

Asif Nawaz Neuro Fuzzy Information Retrieval

8514 руб.
The goal of this book is to let people know about the information retrieval system. It cover the problems in this domain and reviews the solution in current space. It explains how to build the fuzzy inference system in order to score the documents in such a way that most relevant documents will get the higher score against the user's information need. Relevant documents are ranked and then fetched on the basis on these scores. This book provides an overview of fuzzy logic and explains the core concepts underlying fuzzy logic. It also explains the design and implementation strategy of neuro fuzzy inference system for information retrieval by using Adaptive Neuro Fuzzy Inference System (ANFIS) toolbox available in MATLAB. Results and Evaluation are also given at the end for neuro fuzzy inference system and its comparison with the existing techniques for information retrieval.
Jerry Mendel Introduction To Type-2 Fuzzy Logic Control. Theory and Applications Jerry Mendel Introduction To Type-2 Fuzzy Logic Control. Theory and Applications Новинка

Jerry Mendel Introduction To Type-2 Fuzzy Logic Control. Theory and Applications

9993.65 руб.
An introductory book that provides theoretical, practical, and application coverage of the emerging field of type-2 fuzzy logic control Until recently, little was known about type-2 fuzzy controllers due to the lack of basic calculation methods available for type-2 fuzzy sets and logic—and many different aspects of type-2 fuzzy control still needed to be investigated in order to advance this new and powerful technology. This self-contained reference covers everything readers need to know about the growing field. Written with an educational focus in mind, Introduction to Type-2 Fuzzy Logic Control: Theory and Applications uses a coherent structure and uniform mathematical notations to link chapters that are closely related, reflecting the book’s central themes: analysis and design of type-2 fuzzy control systems. The book includes worked examples, experiment and simulation results, and comprehensive reference materials. The book also offers downloadable computer programs from an associated website. Presented by world-class leaders in type-2 fuzzy logic control, Introduction to Type-2 Fuzzy Logic Control: Is useful for any technical person interested in learning type-2 fuzzy control theory and its applications Offers experiment and simulation results via downloadable computer programs Features type-2 fuzzy logic background chapters to make the book self-contained Provides an extensive literature survey on both fuzzy logic and related type-2 fuzzy control Introduction to Type-2 Fuzzy Logic Control is an easy-to-read reference book suitable for engineers, researchers, and graduate students who want to gain deep insight into type-2 fuzzy logic control.
Dehmer Matthias Analysis of Complex Networks. From Biology to Linguistics Dehmer Matthias Analysis of Complex Networks. From Biology to Linguistics Новинка

Dehmer Matthias Analysis of Complex Networks. From Biology to Linguistics

20992.12 руб.
Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.
Smirnov Aleksandr, Shtreker Evgeniy, Pavel Nabrodov Projekt Von Aleksandr Smirnov Aleksandr, Shtreker Evgeniy, Pavel Nabrodov Projekt Von Aleksandr Новинка

Smirnov Aleksandr, Shtreker Evgeniy, Pavel Nabrodov Projekt Von Aleksandr

8789 руб.
The monograph will be interesting for young scientists in the field of telecommunications In the work are the main principles of parallel telecommunication systems. In the basis of these principles lies parallel processing of data and development of telecommunication devices on a parallel basis. For the following theoretical base was used system of residual classes. Data in this system can be processed in parallel without transfer digits. Elements of telecommunications proposed to implement in the neural network which has parallelism and the ability to learn or setting up an obstacle. In the basis of the book lies the theory of transfer and processing of parallel data by parallel elements of telecommunications. The book offered some of neural networks and put some outstanding issues, such as training of neural networks in the mirror mathematics.
Oualid Hammi Behavioral Modeling and Predistortion of Wideband Wireless Transmitters Oualid Hammi Behavioral Modeling and Predistortion of Wideband Wireless Transmitters Новинка

Oualid Hammi Behavioral Modeling and Predistortion of Wideband Wireless Transmitters

7937.88 руб.
Covers theoretical and practical aspects related to the behavioral modelling and predistortion of wireless transmitters and power amplifiers. It includes simulation software that enables the users to apply the theory presented in the book. In the first section, the reader is given the general background of nonlinear dynamic systems along with their behavioral modelling from all its aspects. In the second part, a comprehensive compilation of behavioral models formulations and structures is provided including memory polynomial based models, box oriented models such as Hammerstein-based and Wiener-based models, and neural networks-based models. The book will be a valuable resource for design engineers, industrial engineers, applications engineers, postgraduate students, and researchers working on power amplifiers modelling, linearization, and design.

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A comprehensive reference giving a thorough explanation of propagation mechanisms, channel characteristics results, measurement approaches and the modelling of channels Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular communication systems, RFID and ad hoc wireless communication systems. It gives a detailed introduction to aspects of channels before presenting the novel estimation and modelling techniques which can be used to achieve accurate models. To systematically guide readers through the topic, the book is organised in three distinct parts. The first part covers the fundamentals of the characterization of propagation channels, including the conventional single-input single-output (SISO) propagation channel characterization as well as its extension to multiple-input multiple-output (MIMO) cases. Part two focuses on channel measurements and channel data post-processing. Wideband channel measurements are introduced, including the equipment, technology and advantages and disadvantages of different data acquisition schemes. The channel parameter estimation methods are then presented, which include conventional spectral-based estimation, the specular-path-model based high-resolution method, and the newly derived power spectrum estimation methods. Measurement results are used to compare the performance of the different estimation methods. The third part gives a complete introduction to different modelling approaches. Among them, both scattering theoretical channel modelling and measurement-based channel modelling approaches are detailed. This part also approaches how to utilize these two modelling approaches to investigate wireless channels for conventional cellular systems and some new emerging communication systems. This three-part approach means the book caters for the requirements of the audiences at different levels, including readers needing introductory knowledge, engineers who are looking for more advanced understanding, and expert researchers in wireless system design as a reference. Presents technical explanations, illustrated with examples of the theory in practice Discusses results applied to 4G communication systems and other emerging communication systems, such as relay, CoMP, and vehicle-to-vehicle rapid time-variant channels Can be used as comprehensive tutorial for students or a complete reference for engineers in industry Includes selected illustrations in color Program downloads available for readers Companion website with program downloads for readers and presentation slides and solution manual for instructors Essential reading for Graduate students and researchers interested in the characteristics of propagation channel, or who work in areas related to physical layer architectures, air interfaces, navigation, and wireless sensing
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