Wavelet Neural Network Based on Improved Particle Swarm Algorithm

摘要：

In allusion to the shortcoming, easily falling into the local optimum, of basic particle swarm algorithm, this paper proposes an improved particle swarm algorithm, and applies it to wavelet neural network to optimize each parameter of the wavelet neural network. New algorithm improves basic particle swarm algorithm from three aspects: firstly, introduce inertial weight factor, and use linearly decreasing weight strategy to weigh two aspects, the convergence precision and convergence rate, of the search capability; secondly, use individual average extremum instead of individual extrema to expand the cognition scope of the particles,which makes the particles can obtain more information to adjust own state; finally, introduce the thought of cross in the genetic algorithm to keep diversity of particle swarm, in order to ensure that it is not easy to fall into the local optimum for the algorithm. The simulation results show that the wavelet neural network based on improved particle swarm algorithm has very good approximation ability and convergence speed.

关键词： particle swarm algorithm wavelet neural network genetic algorithm the function approximation

作者: Qingkun Song Lina Liu

作者单位: School of Automation,Harbin University of Science and Technology,Harbin China

会议类型: 国际会议

会议名称: The 6th International Forum on Strategic Technology(IFOST 2011)(第六届国际战略技术论坛)

会议地点: 哈尔滨

会议语种:英文

页码: 1000-1004

在线出版日期: 2011-08-22（万方平台首次上网日期，不代表论文的发表时间）