基于自适应云粒子群算法的Wiener模型辨识  

Identification of Wiener model based on adaptive cloud model particle swarm optimization algorithm

作　　者：张朝龙[1] 余春日[1] 江善和[1] 李彦梅[1] 杨伟[1] 吴文进[1] 

机构地区：[1]安庆师范学院物理与电气工程学院,安徽安庆246011

出　　处：《计算机应用研究》2012年第11期4041-4044,4049,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(10974139);安徽省高校省级自然科学研究重点资助项目(KJ2010A227);安徽省高校省级优秀青年人才基金资助项目(2012SQRL112);安庆师范学院青年科研基金资助项目(KJ201104)

摘　　要：针对非线性系统Wiener模型的系统辨识问题,提出一种基于自适应云模型的粒子群优化(ACMPSO)算法的辨识方法。ACMPSO算法利用云模型实现优秀粒子的遗传和进化操作,根据进化状况动态调整云模型的参数,自适应地控制云模型算法的寻优范围和精度,有较强的全局搜索和局部求精能力。仿真实验证明该算法寻优精度高于其他主要PSO算法;将该算法应用于Wiener模型的系统辨识,通过实验证明了该辨识方法优于当前其他方法。To identify the nonlinear system Wiener model,this paper put forward a new method based on adaptive cloud model particle swarm optimization（ACMPSO） algorithm,which introduced cloud model algorithm into the convergence process of PSO algorithm.The ACMPSO algorithm realized excellent particles＇genetic and evolutional operation,adjusted cloud model＇s parameters according to evolutionary status and controled the search range and accuracy adaptively,therefore ACMPSO had better performance in global search and local optimization.The simulations prove the ACMPSO has better optimization perfor-mance than the other main PSOs.And this paper provided a numerical simulation of Wiener model to prove the method has better identify performance than the other methods.

关 键 词：云模型 粒子群优化 WIENER模型 系统辨识 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]