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

Research output: Contribution to journalArticle

134 Citations (Scopus)
LanguageEnglish
Pages755-766
JournalIEEE Transactions on Fuzzy Systems
Volume14
Issue number6
DOIs
Publication statusPublished - 1 Dec 2006

Cite this

@article{b85a5b767bae4a25b6d3d469b077be3d,
title = "Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms",
author = "G Leng and T McGinnity and G Prasad",
note = "Other Details ------------------------------------ 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.",
year = "2006",
month = "12",
day = "1",
doi = "10.1109/TFUZZ.2006.877361",
language = "English",
volume = "14",
pages = "755--766",
journal = "IEEE Transactions on Fuzzy Systems",
issn = "1063-6706",
number = "6",

}

Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms. / Leng, G; McGinnity, T; Prasad, G.

In: IEEE Transactions on Fuzzy Systems, Vol. 14, No. 6, 01.12.2006, p. 755-766.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

AU - Leng, G

AU - McGinnity, T

AU - Prasad, G

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PY - 2006/12/1

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DO - 10.1109/TFUZZ.2006.877361

M3 - Article

VL - 14

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EP - 766

JO - IEEE Transactions on Fuzzy Systems

T2 - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

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