摘要
DE算法是一类基于种群的启发式全局搜索技术,该算法原理简单,控制参数少,鲁棒性强,具有良好的优化性能.本文利用差分进化算法对Wiener模型参数进行辨识,把辨识问题等价为以估计参数为优化变量的非线性极小值优化问题,并分析了算法中种群规模NP、缩放因子F、交叉概率CR等控制参数对辨识过程中的全局并行搜索能力和收敛速度的影响,以保证算法的全局收敛性.对Wiener模型的数值仿真结果表明了DE算法在参数辨识问题中的有效性,以及较PSO算法更强的非线性系统辨识能力。
DE algorithm is a population-based heuristic global search technology.The algorithm principle is simple, less control parameters,strong robustness,and good optimization performance. This paper uses differential evolution for parameter identification of Wiener model.The identification problem is equivalent to the nonlinear minimization problem with the estimated parameters as the optimized variables,and analyzes the scale of population NP, zoom factor, crossover probability CR of control parameters in the process of identification of global parallel search ability and the influence of convergence to ensure the global convergence. A numerical simulation results of a Wiener model show that DE algorithm is effective in parameter identification problem,and stronger than PSO in nonlinear system identification ability.
出处
《自动化技术与应用》
2012年第12期1-5,共5页
Techniques of Automation and Applications
关键词
参数辨识
WIENER模型
差分进化算法
粒子群算法
parameter identification
wiener model
differential evolution
particle swarm algorithm
