A Modified Particle Swarm Neural Network Based on Local Chaotic Optimization Strategy

摘要：

BP neural network is an important type of neural network that has good approximation property and generalization ability. However, it often converges to local optima in implementation. The particle swarm optimization (PSO) is a global optimization method. It is integrated into BP neural network to enhance the global problem-solving ability. Premature problems are often encountered in optimization. To alleviate this problem, a chaotic strategy is devised to prevent the combined algorithm from falling into premature state. This hybrid algorithm is named with modified chaotic particle swarm optimization (MCPSO) algorithm. We use it to optimize the parameters of a BP neural network, and then apply this network to Iris data classification problem. Simulation results show that MCPSO-based BP neural network can not only optimize the networks parameters and thresholds, but also can achieve accurate classification results.

关键词： particle swarm optimization neural network classification chaotic strategy

作者: Zhangjun Zhou Lihong Xu Dawei Li

作者单位: Dept.of Control Science and Control Engineering, Tongji University, Shanghai, China 200092

会议类型: 国际会议

会议名称: 2012 Fifth International Symposium on Computational Intelligence and Design 第五届计算智能与设计国际会议 ISCID 2012

会议地点: 杭州

会议语种:英文

页码: 1056-1060

在线出版日期: 2012-10-28（万方平台首次上网日期，不代表论文的发表时间）