A novel hybrid algorithm for creating self-organizing fuzzy neural networks

被引：23

作者：

Khayat, Omid ^{[1
]}

Ebadzadeh, Mohammad Mehdi ^{[1
]}

Shahdoosti, Hamid Reza ^{[1
]}

Rajaei, Ramin ^{[2
]}

Khajehnasiri, Iman ^{[3
]}

机构：

[1] Amirkabir Univ Technol, Dept Biomed Engn, Tehran, Iran

[2] Amirkabir Univ Technol, Dept Comp Engn, Tehran, Iran

[3] Sharif Univ Technol, Dept Elect Engn, Tehran, Iran

来源：

NEUROCOMPUTING | 2009年 / 73卷 / 1-3期

关键词：

Self-organizing; Fuzzy neural network; Genetic algorithm; Particle swarm optimization; Xie-Beni index; FUNCTION APPROXIMATION; MODEL; IDENTIFICATION; RULES;

D O I：

10.1016/j.neucom.2009.06.013

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel hybrid algorithm based on a genetic algorithm and particle swarm optimization to design a fuzzy neural network, named self-organizing fuzzy neural network based on GA and PSO (SOFNNGAPSO), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. The proposed algorithm, as a new hybrid algorithm, consists of two phases. A tuning based on TS's fuzzy model is applied to identify the fuzzy structure, and also a fuzzy cluster validity index is utilized to determine the optimal number of clusters. To obtain a more precision model, GA and PSO are performed to conduct fine tuning for the obtained parameter set of the premise parts and consequent parts in the aforementioned fuzzy model. The proposed algorithm is successfully applied to three tested examples. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：517 / 524

页数：8

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