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 |
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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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