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Neural Fuzzy Control Systems With Structure And Parameter Learning - Chin-teng Lin

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Neural Fuzzy Control Systems With Structure And Parameter Learning

By: Chin-teng Lin

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Hardcover | 1 February 1994

At a Glance

Hardcover
144 Pages

Dimensions(cm)
22.86 x 16.51 x 1.27

Hardcover

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A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed in this book for the realization of a fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. In order to set up this proposed FNN, the author recommends two complementary structure/parameter learning algorithms, a two-phase hybrid learning algorithm and an on-line supervised structure/parameter learning algorithm. Both of the above two learning algorithms require exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications, a Reinforcement Fuzzy Neural Network (RFNN) is further proposed. Computer simulation examples are presented to illustrate the performance and applicability of the proposed FNN, RFNN and their associated learning algorithms for various applications.

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Foreword
Preface
Introduction
Structure of a Fuzzy Neural Network
Hybrid Learning Algorithm for FNN
On-Line Supervised Structure/Parameter
Reinforcement Structure/Parameter Learning for an Integrated Fuzzy Neural Network
Conclusions and Future Research
Appendix A: Convergence Proof of Competitive Learning Algorithm
Appendix B: Approximate Similarity Measure of Fuzzy Sets
Bibliography
Index
Table of Contents provided by Blackwell. All Rights Reserved.

Neural Fuzzy Control Systems With Structure And Parameter Learning

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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