Reinforcement hybrid evolutionary learning for TSK-type neuro-fuzzy controller design

Yung Chi Hsu, Sheng-Fuu Lin

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

Abstract

This paper proposes a recurrent TSK-type neuro-fuzzy controller (TNFC) with reinforcement hybrid evolutionary learning algorithm (R-HELA). The proposed R-HELA combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA) to perform the structure/parameter learning for constructing the TNFC dynamically. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. Illustrative example is conducted to show the performance and applicability of the proposed R-HELA method.

Original languageEnglish
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Edition1 PART 1
DOIs
StatePublished - 1 Dec 2008
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 6 Jul 200811 Jul 2008

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume17
ISSN (Print)1474-6670

Conference

Conference17th World Congress, International Federation of Automatic Control, IFAC
CountryKorea, Republic of
CitySeoul
Period6/07/0811/07/08

Keywords

  • Adaptive control by neural networks
  • Learning theory

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