An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network

被引：181

作者：

Leng, G ^{[1
]}

McGinnity, TM ^{[1
]}

Prasad, G ^{[1
]}

机构：

[1] Univ Ulster, Sch Comp & Intelligent Syst, Intelligent Syst Engn Lab, Londonderry BT48 7JL, North Ireland

来源：

FUZZY SETS AND SYSTEMS | 2005年 / 150卷 / 02期

关键词：

fuzzy rule extraction; EBF; self-organising fuzzy neural network; TS model;

D O I：

10.1016/j.fss.2004.03.001

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents a hybrid neural network, called the self-organising-fuzzy neural network (SOFNN), to extract fuzzy rules from the training data. The first hidden layer of this network consists of ellipsoidal basis function (EBF) neurons. Every EBF neuron in the SOFNN has both a centre vector and a width vector. Neurons are organised by the network itself. The methods of the structure and parameter learning, based on new adding and pruning techniques and a recursive learning algorithm. are simple and effective. with a high accuracy and a compact structure. Simulations show that the SOFNN has the capability to encode fuzzy rules in the resulting network. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：211 / 243

页数：33

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