基于粒子群优化算法的非线性系统模型参数估计  

A Method of Parameter Estimation in a Nonlinear System Model Based on Particle Swarm Optimization

作　　者：陆克中[1] 吴璞[1] 王汝传[1] 

机构地区：[1]南京邮电大学计算机科学与技术系

出　　处：《计算机技术与发展》2008年第6期57-59,共3页Computer Technology and Development

基　　金：国家自然科学基金资助项目(70271050)

摘　　要：非线性模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值。由于粒子群算法是一种有效的解决优化问题的群集智能算法,它的突出特点是操作简便、容易实现且全局搜索功能较强,故将粒子群优化算法用于非线性系统模型参数估计,并通过对3种典型的非线性模型的参数估计进行了验证。实验结果表明:粒子群优化算法参数估计精度高,是一种有效的参数估计方法。Estimation of nonlinear model parameters is a tough searching problem. Unfortunately, the traditional approaches easily get stuck in a local minimum. Considering that the particle swarm optimization （PSO） algorithm is quite simple and easy to implement, it was used to estimate the nonlinear model parameters in this paper. Here three model of nonlinear systems were estimated by PSO algorithm and simulations demonstrated that PSO algorithm is an effective way for nonlinear system parameter estimation with global optimal.

关 键 词：粒子群优化 非线性系统 参数估计 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]