A Simplified Structure Evolving Method for Fuzzy System structure learning

Di Wang, Xiao Jun Zeng, John A. Keane

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

1 Scopus citations

Abstract

This paper proposes a Simplified Structure Evolving Method (SSEM) for Fuzzy Systems, which improves our previous work of Structure Evolving Learning Method for Fuzzy Systems (SELM [1]). SSEM keeps all the advantages of SELM [1] and improve SELM by starting with the simplest fuzzy rule set with only one fuzzy rule (instead of 2n fuzzy rules in SELM) as the starting point. By doing this SSEM is able to select the most efficient partitions and the most efficient attributes as well for system identification. This improvement enables fuzzy systems applicable to high dimensional problems. Benchmark examples with high dimension inputs are given to illustrate the advantages of the proposed algorithm.

Original languageBritish English
Title of host publicationIEEE SSCI 2011
Subtitle of host publicationSymposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems
Pages46-53
Number of pages8
DOIs
StatePublished - 2011
EventSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011 - Paris, France
Duration: 11 Apr 201115 Apr 2011

Publication series

NameIEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems

Conference

ConferenceSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011
Country/TerritoryFrance
CityParis
Period11/04/1115/04/11

Keywords

  • evolved learning
  • fuzzy systems
  • Mamdani Fuzzy Systems
  • system identification

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