Cooperatively coevolving differential evolution for compensatory neural fuzzy networks

Author

Cheng-hung Chen ; Wen-Hsien Chen

Author_Institution

Dept. of Electr. Eng., Nat. Formosa Univ., Yunlin, Taiwan

fYear

2013

fDate

6-8 Dec. 2013

Firstpage

264

Lastpage

267

Abstract

This study presents a cooperatively coevolving differential evolution (CCDE) learning algorithm to optimize the parameters of a compensatory neural fuzzy network (CNFN). CCDE decomposes the fuzzy system into multiple subpopulations where each subpopulation represents a fuzzy rule set, and each individual within each subpopulation evolves by differential evolution (DE) separately. The proposed CCDE uses cooperative behavior among multiple subpopulations for combining their information and building the complete fuzzy system to accelerate the search and increase global search capacity.

Keywords

evolutionary computation; fuzzy neural nets; fuzzy set theory; search problems; CCDE learning algorithm; CNFN; compensatory neural fuzzy network; cooperatively coevolving differential evolution; fuzzy rule set; global search capacity; Educational institutions; Fuzzy systems; Input variables; Neural networks; Noise; Training; Vectors; Cooperative coevolution; differential evolution; neural fuzzy networks; water bath temperature system;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Theory and Its Applications (iFUZZY), 2013 International Conference on

Conference_Location

Taipei

Type

conf

DOI

10.1109/iFuzzy.2013.6825447

Filename

6825447