TRAINING MATRIX PARAMETERS BY PARTICLE SWARM OPTIMIZATION USING A FUZZY NEURAL NETWORK FOR IDENTIFICATION

被引：0

作者：

Shafiabady, Niusha ^{[1
]}

Teshnehlab, M. ^{[2
]}

Shooredeli, M. Aliyari ^{[3
]}

机构：

[1] Azad Univ Sci & Res Ctr, Dept Mechatron Engn Technol, Tehran, Iran

[2] KN Toosi Univ Technol, Tehran, Iran

[3] KN Toosi Univ, Tehran, Iran

来源：

ICIAS 2007: INTERNATIONAL CONFERENCE ON INTELLIGENT & ADVANCED SYSTEMS, VOLS 1-3, PROCEEDINGS | 2007年

关键词：

Identification; Radial Basis Function Fuzzy Neural Network; Particle Swarm Optimization; Least Square; Recursive Least Square;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article Particle Swarm Optimization that is a population-based method is applied to train the matrix parameters that are standard deviation and centers of Radial Basis Function Fuzzy Neural Network. We have applied Least Square and Recursive Least Square in training the weights of this fuzzy neural network. There are four sets of data used to examine and prove that Particle Swarm Optimization is a good method for training these complicated matrices as antecedent part parameters.

引用

页码：188 / +

页数：2

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