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1316 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 7, NO. 5 , SEPTEMBER 1996

Neural Fuzzy Systems: A Neuro-Fuzzy Synergism toIntelligent Systems-Chin-T eng L in and C.S.George L ee.(Englewood Cliffs, NJ : Prentice-Hall,1996,813pp.,hardbound, $80.00, I SB N 0-13-235169-2).

This textbook provides engineers, scientists, and students involvedin fuzzy systems, neural networks, and fuzzy neural integrated sys-tems with a current and comprehensive account of the basic principlesunderlying the analysis and synthesis of fuzzy neural integratedsystems. The book is the outgrowth of lecture notes for courses taughtby the authors at Purdue University, West Lafayette, IN, and theNational Chiao-Tung University in Taiwan. The mathematical levelis suited for first-year graduate students in engineering or computer

Logic Control Systems; Applications of Fuzzy Theory; Introductionto Artificial Neural Networks; Feedforward Networks and Super-vised Learning; Single-L ayer Feedback Networks and AssociativeMemories; Unsupervised L earning Networks; R ecurrent Neural Net-works; Genetic A lgorithms; Structure-Adaptive Neural Networks;Applications of Neural Networks; Integratlng Fuzzy Systems andNeural Networks; Neural-Network-Based Fuzzy Systems; NeuralFuzzy Controllers; Fuzzy L ogic-B ased Neural Network Models; andFuzzy Neural Systems for Pattern Recognition.

science. The book provides numerous worked out examples andend of chapter problems. A solutions manual is available from thepublisher.The material in this textbook i s organized in three major paasand two appendixes. Part I consists of eight chapters which coversfundamental concepts and operations of fuzzy sets, fuzzy relations,fuzzy measures, possibility theory, fuzzy logic and approximate

Artificial Neural Networks for M odelling and Controlof Nonl inear Systems-J ohan A . K . Suykens,JOOs L. Vandewallee$ and Bart L. R. Moor. (Boston,MA: K luwer, 1996, 247 PP.3 hardbound, $98.00,ISBN 0-7923-9678-2).

reasoning, and their application to control, pattern recognition, andexpert systems. Part I1 (eight chapters) covers artificial neural net-works including multilayer perceptron nets and recurrent networks.Supervised, unsupervised, and reinforcement learning methods fortraining neural networks are also covered. This part also exploresgenetic algorithms for neural-net training and structure optimization.Part I11 (five chapters) covers three major neural fuzzy integratedsystems and their rationale for integration. Here, neural fuzzy controlsystems, fuzzy neural networks for pattern recognition, and fuzzy neu-ral hybrid systems are discussed and explored. Finall y, Appendixes Aand B illustrate the computer simulations of fuzzy logic systems andneural networks using the MATL AB Fuzzy Logic Toolbox and theNeural Network Toolbox (included on a 3.5PC compatible diskette).The book concludes with a bibliography of over I100 references anda subject index.The chapter titles are as follows: Introduction; Basics of FuzzySets; Fuzzy Relations; Fuzzy M easures; Possibil ity Theory andFuzzy Arithmetic; Fuzzy L ogic and Approximate Reasoning; Fuzzy

Publisher Item Identifer S 1045-9227(96)08062-9

This book deals with the useof artif icial neural networks for modelingand control purposes. I t aims at presenting both classical and newmethods of nonlinear system identification and neural control withemphasis on the fundamental concepts. T he book is based on thefirst authors Ph.D. dissertation.The major contribution of this work is the so-called NL q theorywhich serves as a unifying framework for stability analysis andsynthesis of nonlinear systems that contain linear and static nonlinearoperators that satisfy a sector condition. NLq systems are describedby nonlinear state space equations with q layers and hence encompassmost of the currently used feedforward and recurrent networks. Usingneural state-space models, the theory enables the design of controllersbased upon identified models from measured input/output data.The book consists of six chapters and three appendixes: Introduc-tion; Artificial Neural Networks: A rchitecture and Learning Rules;Nonlinear System Identification Using Neural Networks; NeuralNetworks for Control; NLq Theory; General Conclusions and Fu-ture Work; A ppendix A: Generation of n-double Scrolls; AppendixB: Fokker-Planck Learning Machine for Global Optimization; andAppendix C: Proof of NL q Theorems.

1045-9227/96$05.00 0 1996 IEEE