### Abstract

Language | English |
---|---|

Pages | 1477-1493 |

Journal | Neural Networks |

Volume | 17 |

Issue number | 10 |

Publication status | Published - Dec 2004 |

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### Cite this

*Neural Networks*,

*17*(10), 1477-1493.

}

*Neural Networks*, vol. 17, no. 10, pp. 1477-1493.

**An on-line algorithm for creating self-organising fuzzy neural networks.** / Leng, G; Prasad, G; McGinnity, TM.

Research output: Contribution to journal › Article

TY - JOUR

T1 - An on-line algorithm for creating self-organising fuzzy neural networks

AU - Leng, G

AU - Prasad, G

AU - McGinnity, TM

PY - 2004/12

Y1 - 2004/12

N2 - 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

AB - 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

UR - http://www.elsevier.com/locate/neunet

M3 - Article

VL - 17

SP - 1477

EP - 1493

JO - Neural Networks

T2 - Neural Networks

JF - Neural Networks

SN - 0893-6080

IS - 10

ER -