An on-line algorithm for creating self-organising fuzzy neural networks

Research output: Contribution to journalArticle

139 Citations (Scopus)

Abstract

This paper presents a new on-line algorithm for creating a self-organizing fuzzy neural network (SOFNN) from sample patterns to implement a singleton or Takagi-Sugeno (TS) type fuzzy model. The SOFNN is based on ellipsoidal basis function (EBF) neurons consisting of a center vector and a width vector. New methods of the structure learning and the parameter learning, based on new adding and pruning techniques and a recursive on-line learning algorithm, are proposed and developed. A proof of the convergence of both the estimation error and the linear network parameters is also given in the paper. The proposed methods are very simple and effective and generate a fuzzy neural model with a high accuracy and compact structure. Simulation work shows that the SOFNN has the capability of self-organization to determine the structure and parameters of the network automatically. Keywords: EBF; Recursive least squares algorithm; Self-organizing fuzzy neural network; TS model
LanguageEnglish
Pages1477-1493
JournalNeural Networks
Volume17
Issue number10
Publication statusPublished - Dec 2004

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Fuzzy neural networks
Recursive functions
Linear networks
Error analysis
Learning algorithms
Neurons

Cite this

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title = "An on-line algorithm for creating self-organising fuzzy neural networks",
abstract = "This paper presents a new on-line algorithm for creating a self-organizing fuzzy neural network (SOFNN) from sample patterns to implement a singleton or Takagi-Sugeno (TS) type fuzzy model. The SOFNN is based on ellipsoidal basis function (EBF) neurons consisting of a center vector and a width vector. New methods of the structure learning and the parameter learning, based on new adding and pruning techniques and a recursive on-line learning algorithm, are proposed and developed. A proof of the convergence of both the estimation error and the linear network parameters is also given in the paper. The proposed methods are very simple and effective and generate a fuzzy neural model with a high accuracy and compact structure. Simulation work shows that the SOFNN has the capability of self-organization to determine the structure and parameters of the network automatically. Keywords: EBF; Recursive least squares algorithm; Self-organizing fuzzy neural network; TS model",
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An on-line algorithm for creating self-organising fuzzy neural networks. / Leng, G; Prasad, G; McGinnity, TM.

In: Neural Networks, Vol. 17, No. 10, 12.2004, p. 1477-1493.

Research output: Contribution to journalArticle

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