Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.76
Being difficult to determine hidden units’s number and unsuitable to select central position in radial basis function (RBF) layer, Particle Swarm Optimization and Resource Allocation (RAN) were proposed for training RBF neural networks. First, determine units’s number in RBF layer using RAN. Then, optimize RBF parameters such as central position, width and weights based on PSO. The simulation results show that the new method has better approximation ability, the shorter time and the higher precision.
Man Chun-tao, Wang Kun, Zhang Li-yong, "A New Training Algorithm for RBF Neural Network Based on PSO and Simulation Study", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 641-645, doi:10.1109/CSIE.2009.76