Paper Info
Title
A hybrid particle swarm optimization and its application in neural networks
Abstract
In this paper, a novel particle swarm optimization model for radial basis function neural networks (RBFNN) using hybrid algorithms to solve classification problems is proposed. In the model, linearly decreased inertia weight of each particle (ALPSO) can be automatically calculated according to fitness value. The proposed ALPSO algorithm was compared with various well-known PSO algorithms on benchmark test functions with and without rotation. Besides, a modified fisher ratio class separability measure (MFRCSM) was used to select the initial hidden centers of radial basis function neural networks, and then orthogonal least square algorithm (OLSA) combined with the proposed ALPSO was employed to further optimize the structure of the RBFNN including the weights and controlling parameters. The proposed optimization model integrating MFRCSM, OLSA and ALPSO (MOA-RBFNN) is validated by testing various benchmark classification problems. The experimental results show that the proposed optimization method outperforms the conventional methods and approaches proposed in recent literature.
Year
DOI
Venue
2012
10.1016/j.eswa.2011.07.028
Expert Syst. Appl.
Keywords
Field
DocType
hybrid algorithm,proposed optimization model,neural network,benchmark test function,proposed alpso,radial basis function neural,square algorithm,classification problem,proposed optimization method,hybrid particle swarm optimization,proposed alpso algorithm,novel particle swarm optimization,markov chain,particle swarm optimization
Particle swarm optimization,Mathematical optimization,Radial basis function neural,Computer science,Markov chain,Least mean square algorithm,Artificial intelligence,Inertia,Artificial neural network,Class separability,Machine learning
Journal
Volume
Issue
ISSN
39
1
0957-4174
Citations 
PageRank 
References 
18
0.61
25
Authors
3
Name
Order
Citations
PageRank
S. Y. S. Leung122713.99
Yang Tang2131064.50
W. K. Wong395749.71