摘要
非线性回归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6种非线性回归模型的参数估计进行了验证。实验结果表明:粒子群优化算法是一种有效的参数估计方法。
Estimation of nonlinear regression 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 regression model parameters in this paper. Here six different models of nonlinear regression system were estimated by PSO algorithm and simulations demonstrated that PSO algorithm is an effective way for nonlinear system parameter estimation with global optimal.
出处
《计算机技术与发展》
2008年第12期134-136,共3页
Computer Technology and Development
基金
安徽省高校青年教师科研资助项目(2006jql244)
关键词
粒子群优化
非线性系统
参数估计
particle swarm optimization
nonlinear system
parameter estimation
