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Neural Network and Fuzzy Logic

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 0-07-006618-3 Suitable for use in senior-level and first-year graduate-level 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 lms-theory 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 0-07-006619-1) 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, multi-layer perceptron, bayesian techniques, and gaussian processes. All examples are implemented with Netlab, a collection of neural network and pattern recognition M-files.
Submitted: Feb 20, 2004
Modelling, Simulation, and Control of Non-Linear 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 model-based 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 senior-level 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 7-5606-0646-6
Submitted: Dec 24, 2003
Fuzzy and Neural Approaches in Engineering  
By Leften H. Tsoukalas & Robert E. Uhrig, ISBN 0-471-16003-2 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 0-471-19247-3) . 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 M-book to allow the user to experiment with changing the MATLAB codefragments in order to gain a better understanding of the application.This M-book explains the theoretical details and also shows the MATLABimplementation. The M-book was written using the MATLAB N
Submitted: Dec 24, 2003
Fuzzy Control  
By Kevin M. Passino & Stephen Yurkovich, ISBN 0-201-18074-X This book provides a control-engineering perspective on fuzzy control.It is concerned with both the construction of nonlinear controllers forchallenging real-world 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 graduate-level 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 real-world 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 0-13-273350-1 For a graduate-level neural networks course, this book provides a comprehensivefoundation of neural networks, recognizing the multidisciplinary natureof the subject. The material presented is supported with examples, computer-orientedexperiments (many using MATLAB) , and end-of-chapter problems.
Submitted: Dec 24, 2003
Fuzzy Logic: Inteligence, Control, and Information  
By John Yen & Reza Langari, ISBN 0-13-525817-0
Submitted: Dec 24, 2003
Fuzzy Model Identification For Control  
This book presents new approaches to the construction of fuzzy models for model-based 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 real-world 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 model-based 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
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence  
By Jyh-Shing Roger Jang / Chuen-Tsai Sun & Eiji Mizutani, ISBN 0-13-261066-3 Included in Prentice Hall's MATLAB Curriculum Series, this text providesa comprehensive treatment of the methodologies underlying neuro-fuzzyand 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 0-7923-8154-8 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, rule-based 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 real-worldapplications 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 higher-order 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



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