Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18927
Title: On-line fuzzy identification using genetic algorithms
Authors: Chow, KM
Rad, AB
Keywords: Fuzzy identification
Fuzzy indexing
Genetic algorithms
Issue Date: 2002
Publisher: Elsevier Science Bv
Source: Fuzzy sets and systems, 2002, v. 132, no. 2, p. 147-171 How to cite?
Journal: Fuzzy Sets and Systems 
Abstract: The design and implementation of an on-line fuzzy identification method using genetic algorithms (GAs) is reported in this paper. In the proposed algorithm, the rule-table of a fuzzy system is first divided into several independent and much smaller fuzzy systems, which in turn are encoded into separate bit strings for genetic operations. A novel GA updating architecture is then proposed to search the optimal rule-base of these fuzzy systems at each sample interval. The performance of this identification algorithm is evaluated by simulation of a non-linear system. Moreover, an experiment on simple behavior learning of a mobile robot is also reported. The results indicate an improvement in design cycle and convergence to the optimal rule-base within a relatively short period of time.
URI: http://hdl.handle.net/10397/18927
ISSN: 0165-0114
DOI: 10.1016/S0165-0114(02)00059-3
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

15
Last Week
0
Last month
0
Citations as of Aug 14, 2017

WEB OF SCIENCETM
Citations

12
Last Week
0
Last month
0
Citations as of Aug 21, 2017

Page view(s)

26
Last Week
0
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



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