Application of Particle Swarm Optimization in Fussy Neural Networks

摘要：

Particle swarm optimization algorithm is a global optimization technique and a new technology base on swarm brainpower. This ideology comes from manpower anima and evolvement calculation theory. Its algorithm is simple for implement and excellent for application. Particle follow the one which is the best it found in the whole swarm to complete optimize. To solve the adjustable capability of fuzzy controlment and combine with the characteristic of nerve network, so fuzzy neural networks based on particle swarm optimization is designed in this paper. A nonlinear system is identified by the fussy neural networks. The distinguish process of fuzzy nerve network is confirming the precondition parameter and conclusion parameter. Simulation result indicates the great effect and potential in optimization of fuzzy nerve network. Base on this arithmetics speediness and availability, it can be use to practical field.

关键词： particle swarm optimization Fuzzy neural networks Identification

作者: Qingnian Wang Kun Yan Xiaofeng Wan Meiling Yuan

作者单位: Information Engineering Institute Nanchang University Nanchang City,China

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 158-161

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）