Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

Research output: Contribution to journalArticlepeer-review

153 Citations (Scopus)
Original languageEnglish
Pages (from-to)755-766
JournalIEEE Transactions on Fuzzy Systems
Issue number6
Publication statusPublished (in print/issue) - 1 Dec 2006

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This paper proposes a hybrid, genetic algorithm (GA)-based approach to create a fuzzy neural network implementing Takagi-Sugeno fuzzy models. An automatically generated structure is subsequently optimised by a new evolving winner GA based method, where the GA optimises the number of neurons. A hybrid parameter learning approach then adjusts the parameter matrix and the parameters of the membership functions. The importance of the approach is that it can generate a more compact structure than alternative methods, at the expense of being used offline, and is shown to be superior to the OBS (optimal brain surgeon) technique.

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