NEURAL FUZZY CONTROL SYSTEMS WITH STRUCTURE AND PARAMETER LEARNING
by Chin-Teng Lin (Purdue Univ. & Nat'l Chiao-Tung Univ.)
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.
Contents:
- 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
Readership: Engineers and students.
"The book is well written and provides a good tool for bringing the low-level computational power and learning ability of Nns into FLSs ... Postgraduate students, researchers, and practitioners will benefit very much by reading this small-size book."
Spyros Tzafestas
Journal of Intelligent and Robotic Systems, 1996 |
| 144pp |
Pub. date: Feb 1994 |
