Title :
A comparison of PSO and GA combined with LS and RLS in identification using fuzzy gaussian neural networks
Author :
Shafiabady, Niusha ; Teshnehlab, M. ; Shooredeh, M. Aliyari
Author_Institution :
Dept. of Mechatron. Eng., Azad Univ. Sci. & Res. Branch, Tehran, Iran
Abstract :
In this article, a new method for training the parameters is discussed and we have compared the function of particle swarm optimization with genetic algorithm in training the standard deviation and centers in the antecedent part of fuzzy Gaussian neural network. We have applied least square and recursive least square in training the weights of this fuzzy neural network in the conclusion part. There are four sets of data used to examine the proposed learning strategy to achieve the proper learning mode.
Keywords :
Gaussian processes; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); least squares approximations; particle swarm optimisation; GA algorithm; LS method; PSO algorithm; RLS method; fuzzy Gaussian neural network; genetic algorithm; learning strategy; least square method; parameter training; particle swarm optimization; recursive least square method; standard deviation; Fuzzy neural networks; Neural networks; Resonance light scattering; Fuzzy Gaussian Neural Network; Genetic Algorithm; Gradient Descent; Identification; Least Square; Particle Swarm Optimization; Recursive Least Square;
Conference_Titel :
Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-4347-5
Electronic_ISBN :
978-1-4244-4349-9
DOI :
10.1109/ISIE.2009.5217923
