期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2015
卷号：7
期号：1
出版社：International Center for Scientific Research and Studies
摘要：Training Radial Basis Function (RBF) neural network with Particle Swarm Optimization (PSO) was considered as a major breakthrough that overcome the stuck to the local minimum of Back Propagation, time consuming and computation expensive problems of Genetic Algorithm. However, PSO converged too fast, and hence stuck to the local optimum. Furthermore, particles may move to an invisible region. Therefore, to realize the enhancement of the learning process of RBF and overcome these PSO problems, Harmony Search Algorithm (HSA), a new meta-heuristic algorithm was employed to optimize the RBF network and to attain the desired objectives. The study has conducted comparative experiments between the integrated HSA-RBF network and the PSO-RBF network. The results proved that HSA increased the learning capability of RBF neural network in terms of accuracy and correct classification percentage, error convergence rate, and less time consumption with less mean squared error. The new HSA-RBF model provided higher performance in most cases and give promising results with better classification proficiency compared with that of PSO-RBF network
