A similarity-based learning algorithm for fuzzy system identification with a two-layer optimization scheme

Sj Lee*, Xiao Jun Zeng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper presents a similarity-based fuzzy learning approach with a two-layer optimization scheme to make fuzzy systems more compact and accuracy. Two ways to improve fuzzy learning algorithms are considered in this paper, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned, the purpose aims at refining the fuzzy rule base by the similarity analysis of fuzzy sets, fuzzy numbers, fuzzy membership functions or fuzzy rules. Through the similarity analysis, the complete rules can be probably kept by decreasing the redundant rules in the rule base of fuzzy systems. Moreover, the optimization scheme can be regarded as a two-layer parameters optimization in the entire work, because the parameters of the initial fuzzy model have been fine tuning by two phases gradation on layer for discovering a better local minimum.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
DOIs
StatePublished - 23 Oct 2012
Event2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
CountryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Keywords

  • fuzzy set
  • fuzzy system identification
  • optimization
  • similarity analysis

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