基于APSO算法的参数辨识与优化  

Parameter Identification and Optimization Based on Adaptive Particle Swarm Optimization Algorithm

作　　者：吴燕翔[1] 李晓斌[1] 孙海燕[1] 

机构地区：[1]上海水产大学工程学院,上海200090

出　　处：《科学技术与工程》2008年第14期3777-3782,共6页Science Technology and Engineering

基　　金：国家科技攻关计划项目(2002BA901A28);上海水产大学博士科研启动基金资助

摘　　要：将自适应粒子群优化(APSO)算法应用在系统辨识和参数优化中,定性地分析系统参数空间范围,把系统辨识和参数优化问题转化为参数空间寻优,利用APSO算法在寻优过程中有效避免局部最优的特点,在整个参数空间内并行寻找获得系统参数的最优解。通过对多种模型的仿真实验研究表明,APSO算法在系统辨识和参数优化问题中优于原有的GA和PSO方法。The adaptive particle swarm optimization （APSO） algorithm is used in system identification and parameter optimization. The problems of system identification and parameter optimization can be viewed as optimization problems in parameter space by qualitatively analyze the scope of system parameter space. The adaptive parti- cle swarm optimization algorithm can effectively avoid getting into local optimum and is used to obtain the optimal solution by searching in the whole parameter space in parallel. The simulations were done for different model exam- pies. The experiment results show that adaptive particle swarm optimization algorithm is an effective method better than GA and PSO for system identification and parameter optimization.

关 键 词：粒子群优化 自适应 系统辨识 参数优化 

分 类 号：TP273.1[自动化与计算机技术—检测技术与自动化装置]