Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/4116
Title: A self-organizing fuzzy controller with a fixed maximum number of rules and an adaptive similarity factor
Authors: Dias, Joana Matos 
Dourado, António 
Keywords: Empirical research; Control theory; Process control; Self-organizing fuzzy control; On-line learning; Applications
Issue Date: 1999
Citation: Fuzzy Sets and Systems. 103:1 (1999) 27-48
Abstract: This paper proposes a self-organizing fuzzy controller with a broad generality for minimum phase and stable systems. The controller learns the rules on-line with a minimum knowledge about the process. The rule base is built and permanenetly actualized from input-output real time data and has a fixed maximum number of rules (FMNR). An (on-line) adaptive similarity factor implements a special efficient inference technique. Feedforward and predictive effect is introduced in fuzzification and defuzzification stages. The defuzzification is carried out in such a way that as the learning process progresses the interval of the control becomes more and more accurate. Results are shown concerning simulations for non-linear SISO, MISO and MIMO systems a nd a r eal e xperimental application using a low-cost microcomputer.
URI: http://hdl.handle.net/10316/4116
Rights: openAccess
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais

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