Parameter Estimation of Complex Functions Based on Quantum-behaved Particle Swarm Optimization Algorithm

摘要：

This paper, quantum-behaved particle swarm optimization (QPSO) algorithm is developed for some serious disadvantages of traditional parameter estimation methods of complex functions in statistics.QPSO algorithm is an improved algorithm of particle swarm optimization algorithm (PSO).QPSO algorithm and PSO algorithm are detailed introduced and studied.A new method that using least-squares estimation of complex functions based on QPSO algorithm is developed.Several tests are made by computer.It indicates that QPSO algorithm can estimate parameters of complex functions correctly.It can calculate simply and constringe fast.Through comparing with traditional PSO algorithm,QPSO algodthms advantages are testified.

关键词： Particle Swarm Optimization Quantumbehaved Particle Swarm Optimization Parameter Estimation

作者: Min Xu Wenbo Xu

作者单位: School of Science,Jiangnan University,Wuxi,Jiangsu,214122,China School of Information Technology,Jiangnan University,Wuxi,Jiangsu,214122,China

会议类型: 国际会议

会议名称: 2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会(2008 International Symposium on Distributed Computing and Applications for Business Engineering and Science)

会议地点: 大连

会议语种:英文

页码: 591-596

在线出版日期: 2008-07-27（万方平台首次上网日期，不代表论文的发表时间）