Please use this identifier to cite or link to this item:
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.
Rights: openAccess
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
filee5036559fba6462ca11061a93016b9a1.pdf1.31 MBAdobe PDFView/Open
Show full item record

Page view(s)

checked on Jun 12, 2019

Download(s) 50

checked on Jun 12, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.