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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Leng, G, Prasad, G and McGinnity, TM (2004) An on-line algorithm for creating self-organising fuzzy neural networks. Neural Networks, 17 (10). pp. 1477-1493. [Journal article]

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URL: http://dx.doi.org/10.1016/j.neunet.2004.07.009

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

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:8177
Deposited By:Professor Girijesh Prasad
Deposited On:01 Feb 2010 12:08
Last Modified:02 Feb 2012 15:15

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