Neural fuzzy control of unstable nonlinear systems

Chin Teng Lin*, Cheng Jian Lin, I. Fang Chung

*此作品的通信作者

研究成果: Conference article同行評審

2 引文 斯高帕斯(Scopus)

摘要

A Fuzzy Adaptive Learning COntrol Network (FALCON) is proposed for the realization of a fuzzy logic control system. An on-line structure/parameter learning algorithm, called FALCON-ART, can on-line partition the input/output spaces, tune membership functions and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these. The FALCON-ART requires exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a Reinforcement Fuzzy Adaptive Learning COntrol Network (RFALCON) is further proposed. By combining a proposed on-line supervised structure/parameter learning technique, the temporal difference method, and the stochastic exploratory algorithm, a on-line supervised structure/parameter learning algorithm, called RFALCON-ART, is proposed for constructing the RFALCON dynamically.

原文English
頁(從 - 到)3666-3671
頁數6
期刊Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
4
DOIs
出版狀態Published - 1995
事件Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Part 2 (of 5) - Vancouver, BC, Can
持續時間: 22 10月 199525 10月 1995

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