TY - GEN
T1 - A comparison of PSO and GA combined with LS and RLS in identification using fuzzy gaussian neural networks
AU - Shafiabady, Niusha
AU - Teshnehlab, M.
AU - Shooredeh, M. Aliyari
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Fuzzy gaussian neural network
KW - Genetic algorithm
KW - Gradient descent
KW - Identification
KW - Least square
KW - Particle swarm optimization
KW - Recursive least square
UR - http://www.scopus.com/inward/record.url?scp=77950169982&partnerID=8YFLogxK
U2 - 10.1109/ISIE.2009.5217923
DO - 10.1109/ISIE.2009.5217923
M3 - Conference Paper published in Proceedings
SN - 9781424443499
VL - 1
T3 - IEEE International Symposium on Industrial Electronics
SP - 2081
EP - 2086
BT - Proceedings - IEEE ISIE 2009, IEEE International Symposium on Industrial Electronics
T2 - IEEE International Symposium on Industrial Electronics, IEEE ISIE 2009
Y2 - 5 July 2009 through 8 July 2009
ER -