NDLI: Online global learning in direct fuzzy controllers

Content Provider	IEEE Xplore Digital Library
Author	Pomares, H. Rojas, I. Gonzalez, J. Damas, M. Pino, B. Prieto, A.
Copyright Year	1993
Abstract	A novel approach to achieve real-time global learning in fuzzy controllers is proposed. Both the rule consequents and the membership functions defined in the premises of the fuzzy rules are tuned using a one-step algorithm, which is capable of controlling nonlinear plants with no prior offline training. Direct control is achieved by means of two auxiliary systems: The first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input-output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed, avoiding overfitting.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	218
Ending Page	229
Page Count	12
File Size	561717
File Format	PDF
ISSN	10636706
Volume Number	12
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy control Control systems Fuzzy systems Adaptive control Adaptive systems Tuning Error correction Real time systems Programmable control Fuzzy logic
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Control and Systems Engineering Computational Theory and Mathematics

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