Neural Network Design 

By Martin T. Hagan, Howard B. Demuth & Mark Beale. This text provides a clear and detailed survey of basic neural network architectures and learning rules. The authors emphasize mathematical analysis of networks, methods for training networks, and application of networks to practical engineering problems in pattern recognition, signal processing, and control systems. Submitted: Feb 20, 2004

Neural Network Fundamentals with Graphs, Algorithms, and Applications 

By N. K. Bose & P. Liang, ISBN 0070066183 Suitable for use in seniorlevel and firstyear graduatelevel courses,as well as for professionals, this text presents the fundamentals of neuralnetwork theory for diverse applications. The reader is guided from neurosciencefundamentals, graph theory, and algorithms, to detailed analysis of perceptronand lmstheory based on neural networks, multilayer feedforward networks,computational learning theory, and other topics. The last chapter presentsselected applications of the theory covered. MATLAB and the Neural NetworkToolbox, which are available from from The MathWorks, are used throughoutthe text and in a solutions manual (ISBN 0070066191) to create graphicsand solve problems. Submitted: Dec 24, 2003

Netlab: Algorithms for Pattern Recognition 

By Ian T. Nabney. Written for courses in pattern recognition and neural networks, this book discusses the theory and practical application of neural networks. Topics covered include parameter optimization algorithms, density modeling, single layer networks, multilayer perceptron, bayesian techniques, and gaussian processes. All examples are implemented with Netlab, a collection of neural network and pattern recognition Mfiles. Submitted: Feb 20, 2004

Modelling, Simulation, and Control of NonLinear Dynamical Systems 

By Patricia Melin & Oscar Castillo. Written for practicing engineers and advanced students, this book discusses the modeling, simulation, and control of nonlinear dynamic systems using soft computing methods and fractal theory. Topics covered include fuzzy logic and neural networks, adaptive modelbased control, and automated mathematical modeling and simulation. MATLAB and the Fuzzy Logic and Neural Network Toolboxes are used to solve numerous sample problems throughout the text. Submitted: Feb 20, 2004

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models 

Written for seniorlevel undergraduates, graduate students, and practicing engineers and scientists, this textbook provides an introduction to, as well as a detailed presentation of, the field of learning from experimental data and soft computing. The fundamental approaches and concepts common to neural networks, support vector machines, and fuzzy logic are discussed individually and as a connected whole. The theory presented in each chapter is reinforced with practical examples, exercises, and simulation experiments. Submitted: Oct 01, 2001

The Analysis and Design of Systems Using MATLAB: Neural Network 

By Lou Shuntian, ISBN 7560606466 Submitted: Dec 24, 2003

Fuzzy and Neural Approaches in Engineering 

By Leften H. Tsoukalas & Robert E. Uhrig, ISBN 0471160032 This book integrates the two technologies of fuzzy logic systems and neuralnetworks. It presents the fundamentals of both technologies, and demonstrateshow to combine their unique capabilities for the greatest advantage. Thebook highlights a wide range of dynamic possibilities and offers numerousexamples to illustrate key concepts. The supplement, MATLAB Supplementto Fuzzy and Neural Approaches in Engineering, by J. Wesley Hines is alsoavailable from John Wiley & Sons, Inc. (ISBN 0471192473) . This supplementcontains numerous examples that demonstrate the practical implementationof neural, fuzzy, and hybrid processing techniques using MATLAB. CompanionSoftware: The author of the supplement, J. Wesley Hines, has includedan Mbook to allow the user to experiment with changing the MATLAB codefragments in order to gain a better understanding of the application.This Mbook explains the theoretical details and also shows the MATLABimplementation. The Mbook was written using the MATLAB N Submitted: Dec 24, 2003

Fuzzy Control 

By Kevin M. Passino & Stephen Yurkovich, ISBN 020118074X This book provides a controlengineering perspective on fuzzy control.It is concerned with both the construction of nonlinear controllers forchallenging realworld applications and the gaining of a fundamental understandingof the dynamics of fuzzy control systems so that we can mathematicallyverify their properties before implementation. The book emphasizes engineeringevaluations of performance and comparative analysis with conventionalcontrol methods. Adaptive methods for identification, estimation, andcontrol are introduced. The book contains numerous examples, applications,design and implementation case studies. It also includes introductionsto neural networks, genetic algorithms, expert and planning systems, andintelligent autonomous control. Submitted: Dec 24, 2003

Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook 

Suitable for graduate students and professional engineers, this book provides a comprehensive introduction to the most popular class of neural networks, the multilayer perceptron, and shows how it can be used for system identification and control. The book aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pitfalls and to make proper decisions in all situations. An introductory course in adaptive control is recommended. Submitted: Nov 11, 2001

Principles of Neurocomputing for Science & Engineering 

This book is primarily intended for graduatelevel neural network courses, but may be used at the undergraduate level. The textbook is designed for those who want to understand the underlying principles of artificial neural networks for neurocomputing and for those who want to be able to apply various neurocomputing techniques to solve realworld problems in science and engineering. MATLAB and the Neural Network Toolbox are used extensively to illustrate neurocomputing concepts and in problems provided at the end of each chapter. Submitted: Jun 02, 2001

Neural Networks: A Comprehensive Foundation, 2e 

By Simon Haykin, ISBN 0132733501 For a graduatelevel neural networks course, this book provides a comprehensivefoundation of neural networks, recognizing the multidisciplinary natureof the subject. The material presented is supported with examples, computerorientedexperiments (many using MATLAB) , and endofchapter problems. Submitted: Dec 24, 2003

Fuzzy Logic: Inteligence, Control, and Information 

By John Yen & Reza Langari, ISBN 0135258170 Submitted: Dec 24, 2003

Fuzzy Model Identification For Control 

This book presents new approaches to the construction of fuzzy models for modelbased control. New model structures and identification algorithms are described for the effective use of heterogeneous information in the form of numerical data, qualitative knowledge, and first principle models. The main methods and techniques are illustrated through several simulated examples and realworld applications from chemical and process engineering practice. Submitted: Oct 09, 2003

Fuzzy Model Identification for Control 

By János Abonyi. Written for researchers and professionals in process control and identification, this book presents approaches to the construction of fuzzy models for modelbased control. Topics covered include fuzzy model identification, analysis of fuzzy model structures, and fuzzy models of dynamical systems. In addition, process models used for case studies are included in an appendix. Submitted: Feb 20, 2004

Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques 

By Jeffrey T. Spooner, Manfredi Maggiore, Raúl Ordóñez & Kevin M. Passino. Written for practicing engineers and graduate students, this text brings together adaptive control with neural networks and fuzzy systems for the control of nonlinear systems. The authors present a control methodology that may be verified with mathematical rigor while possessing the flexibility and ease of implementation associated with intelligent control approaches. Submitted: Feb 20, 2004

Intelligent Control Systems Using Soft Computing Methodologies 

This book focuses on the design and analysis of biological and industrial control systems. The book begins with the fundamentals of neural networks, supervised and unsupervised learning, and recurrent networks. It then focuses on the theory and fundamentals of fuzzy logic, implementation, and examples of applications and concludes with the integration of genetic algorithms into the paradigm of soft computing methodologies. MATLAB is used to solve exercises on neural networks. Submitted: Aug 08, 2001

NeuroFuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence 

By JyhShing Roger Jang / ChuenTsai Sun & Eiji Mizutani, ISBN 0132610663 Included in Prentice Hall's MATLAB Curriculum Series, this text providesa comprehensive treatment of the methodologies underlying neurofuzzyand soft computing. The book places equal emphasis on theoretical aspectsof covered methodologies, empirical observations, and verifications ofvarious applications in practice. Submitted: Dec 24, 2003

Fuzzy Modeling for Control 

By Robert Babuska, ISBN 0792381548 This book addresses the modeling of complex, nonlinear, or partially unknownsystems by means of techniques based on fuzzy set theory and fuzzy logic.The author focuses on the development of transparent, rulebased fuzzymodels that can accurately predict the quantities of interest and at thesame time provide insight into the system that generated the data. Topicscovered include the selection of appropriate model structures, the acquisitionof dynamic fuzzy models from process measurements, and the design of nonlinearcontrollers based on fuzzy models. The main features of the presentedtechniques are illustrated by simple examples. In addition, three realworldapplications are described. Submitted: Dec 24, 2003

Fundamentals of Computational Neuroscience 

This book introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. Additionally, it introduces several fundamental network architectures and discusses their relevance, giving some examples of models of higherorder cognitive functions to demonstrate the insight that can be gained with such studies. The book also contains an introductory MATLAB chapter with numerous applicable programs. Submitted: Feb 20, 2004

Neural Engineering 

By Chris Eliasmith & Charles H. Anderson. This text is written for neuroscientists and engineers, physicists, and computer scientists interested in applying techniques of their fields to neurobiological systems. This book provides a framework for constructing neurobiological simulations through discussions of system descriptions, design specification, and implementation. Submitted: Feb 20, 2004

Les Reseaux de Neurones 

Designed for students in cognitive sciences, psychology, computer sciences, and engineering, this book can serve as an introduction to neural networks as well as a reference. An appendix of MATLAB examples is included. Submitted: Nov 11, 2001

