Neural Fuzzy Control Systems with Structure and Parameter Learning

Chin-Teng Lin

Research output: Book/ReportBookpeer-review

Abstract

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 these 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.
Original languageEnglish
PublisherWorld Scientific
Number of pages144
ISBN (Print)9789810216139
StatePublished - Feb 1994

Fingerprint Dive into the research topics of 'Neural Fuzzy Control Systems with Structure and Parameter Learning'. Together they form a unique fingerprint.

Cite this