Progress in on-line adaptive, learning and evolutionary strategies for fuzzy logic control

Author

Fei, Minrui ; Ho, S.L.

Author_Institution

Sch. of Autom., Shanghai Univ., China

Volume

2

fYear

1999

fDate

1999

Firstpage

1108

Abstract

In this paper, the eight kinds of on-line adaptive, learning and evolutionary strategies for fuzzy logic control are systematically introduced. All these afore-mentioned strategies have some drawbacks in terms of generalization and formulation. Hence a systematic way of combination and hybridization of these strategies will be very useful for improving the learning capacity and performance of algorithms based on these strategies. It is concluded that the orientation of deep-going pathfinding in the generation and modification of fuzzy control rules or models which is principally based on neural networks combined with genetic algorithms or other algorithms should be able to compensate for the disadvantages of neural networks learning

Keywords

fuzzy control; genetic algorithms; learning (artificial intelligence); neural nets; unsupervised learning; clustering algorithms; competitive learning; deep-going pathfinding orientation; evolutionary strategy; expert learning; fuzzy control rules; fuzzy logic control; genetic algorithms; hybrid learning; learning capacity; learning strategy; neural networks; neural networks learning; on-line adaptive strategy; reinforcement learning; Adaptive control; Automatic control; Control systems; Fuzzy control; Fuzzy logic; Fuzzy systems; Learning; Neural networks; Programmable control; Temperature control;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics and Drive Systems, 1999. PEDS '99. Proceedings of the IEEE 1999 International Conference on

Print_ISBN

0-7803-5769-8

Type

conf

DOI

10.1109/PEDS.1999.792863

Filename

792863