摘要
基于极大似然法的参数估计实质上是一个复杂的非线性优化问题,传统的优化方法计算效率较低且容易陷入局部极值。该文将单纯形法与并行遗传算法相结合,提出了一种新的并行遗传算法,可以有效地防止搜索过程中的早熟现象。应用于系统初始状态未知时的参数估计问题,获得了满意的结果。
The parameter estimation based on the maximum likelihood method is a complicated nonlinear optimization problem actually.The traditional optimization algorithms are apt to be trapped into local minima,and the computation efficiencies are quite low.In this paper,a new parallel genetic algorithm combining the simplex method with the parallel genetic algorithm is proposed,which can prevent premature convergence effectively and improve the estimation precision and computation efficiency.The proposed algorithm is applied to the parameter estimation problem with unknown initial states of system and satisfactory results are obtained.
出处
《计算机工程与应用》
CSCD
北大核心
2005年第19期50-52,共3页
Computer Engineering and Applications
关键词
初始状态
极大似然法
单纯形法
并行遗传算法
initial state,maximum likelihood,simplex method,parallel genetic algorithm
