Neuro-Fuzzy System Design Using Differential Evolution with Local Information

Chin-Teng Lin, Ming-Feng Han, Yang-Yin Lin, Shih-Hui Liao, Jyh-Yeong Chang

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

6 Scopus citations

Abstract

This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on 1/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line clustering algorithm is used for rule generation. The structure learning algorithm generates a new rule which compares the firing strength. Initially, there is no rule in neuro-fuzzy system model. The rules are automatically generated by fuzzy measure. For parameter learning, the parameters are optimized by differential evolution algorithm. Finally, the proposed neuro-fuzzy system with novel differential evolution model is applied in chaotic sequence prediction problem. Results of this paper demonstrate the effectiveness of the proposed model.
Original languageEnglish
Title of host publicationIEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
PublisherIEEE
Pages1003-1006
Number of pages4
ISBN (Print)978-1-4244-7317-5
DOIs
StatePublished - 2011

Publication series

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

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