Study on RBF Neural Network Based on Swarm Intelligence

摘要：

Particle swarm optimization (PSO) is one of swarm intelligence. It was modified by escape of the particle velocity, and a self-adaptive PSO (SAPSO) was proposed to overcome the PSO shortcomings of the premature convergence and the local optimization. The SAPSO is combined with radial basis function (RBF) neural network to form a SAPSON hybrid algorithm. Compared with radial basis function neural network, SAPSON has less adjustable parameters, faster convergence speed, global optimization and higher identification precision in the numerical experiment.

关键词： self-adaptive PSO swarm intelligence hybrid algorithm radial basis function

作者: Jian Guo EDong

作者单位: Wuhan Polytechnic University Wuhan, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)

会议地点: 哈尔滨

会议语种:英文

页码: 108-111

在线出版日期: 2011-01-18（万方平台首次上网日期，不代表论文的发表时间）