Training matrix parameters by particle swarm optimization using a fuzzy neural network for identification

Niusha Shafiabady, M. Teshnehlab, M. Aliyari Shooredeli

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

Abstract

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.

Original languageEnglish
Title of host publication2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007
Pages188-193
Number of pages6
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007 - Kuala Lumpur, Malaysia
Duration: 25 Nov 200728 Nov 2007

Publication series

Name2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007

Conference

Conference2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007
Country/TerritoryMalaysia
CityKuala Lumpur
Period25/11/0728/11/07

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