TY - GEN
T1 - Training matrix parameters by particle swarm optimization using a fuzzy neural network for identification
AU - Shafiabady, Niusha
AU - Teshnehlab, M.
AU - Aliyari Shooredeli, M.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Identification
KW - Least square
KW - Particle swarm optimization
KW - Radial basis function fuzzy neural network
KW - Recursive least square
UR - http://www.scopus.com/inward/record.url?scp=57949109312&partnerID=8YFLogxK
U2 - 10.1109/ICIAS.2007.4658372
DO - 10.1109/ICIAS.2007.4658372
M3 - Conference Paper published in Proceedings
SN - 1424413559
SN - 9781424413553
T3 - 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007
SP - 188
EP - 193
BT - 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007
T2 - 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007
Y2 - 25 November 2007 through 28 November 2007
ER -