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

G Leng; T McGinnity; G Prasad

doi:10.1016/j.fss.2004.03.001

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

Research output: Contribution to journal › Article

Original language	English
Pages (from-to)	211-243
Journal	Fuzzy Sets and Systems
Volume	150
Issue number	2
DOIs	https://doi.org/10.1016/j.fss.2004.03.001
Publication status	Published - 1 Mar 2005

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10.1016/j.fss.2004.03.001

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@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",

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doi = "10.1016/j.fss.2004.03.001",

language = "English",

volume = "150",

pages = "211--243",

journal = "Fuzzy Sets and Systems",

issn = "0165-0114",

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number = "2",

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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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U2 - 10.1016/j.fss.2004.03.001

DO - 10.1016/j.fss.2004.03.001

M3 - Article

VL - 150

SP - 211

EP - 243

JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

SN - 0165-0114

IS - 2

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