An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network

Research output: Contribution to journalArticle

221 Citations (Scopus)
LanguageEnglish
Pages211-243
JournalFuzzy Sets and Systems
Volume150
Issue number2
DOIs
Publication statusPublished - 1 Mar 2005

Cite this

@article{cf05c7f950314e0b87818ebad4fe5a29,
title = "An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network",
author = "G Leng and T McGinnity and G Prasad",
note = "Other Details ------------------------------------ Fuzzy modeling of complex systems requires the extraction of a suitable collection of fuzzy rules from the available data set. This is often a difficult challenge. While neural networks have the ability to learn the fuzzy rules automatically, it is usually not possible to extract the rules from the trained neural network. The importance of this paper is that it presents a new self-organising fuzzy neural network that is able to extract fuzzy rules from the data set, and implement structure and parameter learning, with a high accuracy. The automatic addition and pruning techniques implemented ensure a compact network.",
year = "2005",
month = "3",
day = "1",
doi = "10.1016/j.fss.2004.03.001",
language = "English",
volume = "150",
pages = "211--243",
journal = "Fuzzy Sets and Systems",
issn = "0165-0114",
publisher = "Elsevier",
number = "2",

}

An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. / Leng, G; McGinnity, T; Prasad, G.

In: Fuzzy Sets and Systems, Vol. 150, No. 2, 01.03.2005, p. 211-243.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network

AU - Leng, G

AU - McGinnity, T

AU - Prasad, G

N1 - Other Details ------------------------------------ Fuzzy modeling of complex systems requires the extraction of a suitable collection of fuzzy rules from the available data set. This is often a difficult challenge. While neural networks have the ability to learn the fuzzy rules automatically, it is usually not possible to extract the rules from the trained neural network. The importance of this paper is that it presents a new self-organising fuzzy neural network that is able to extract fuzzy rules from the data set, and implement structure and parameter learning, with a high accuracy. The automatic addition and pruning techniques implemented ensure a compact network.

PY - 2005/3/1

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DO - 10.1016/j.fss.2004.03.001

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JO - Fuzzy Sets and Systems

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