### 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

Original language | English |
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Pages (from-to) | 1477-1493 |

Journal | Neural Networks |

Volume | 17 |

Issue number | 10 |

Publication status | Published - Dec 2004 |

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## Cite this

Leng, G., Prasad, G., & McGinnity, TM. (2004). An on-line algorithm for creating self-organising fuzzy neural networks.

*Neural Networks*,*17*(10), 1477-1493.