运用ADE算法进行Wiener模型辨识  

Adaptive Differential Evolution Identification of Wiener Model

作　　者：熊伟丽[1,2] 许文强[2] 赵兢兢[2] 徐保国[2] 

机构地区：[1]江南大学轻工过程先进控制教育部重点实验室,无锡214122 [2]江南大学物联网工程学院自动化系,无锡214122

出　　处：《系统仿真学报》2013年第5期969-974,982,共7页Journal of System Simulation

基　　金：国家自然科学基金项目(21206053;21276111);博士后基金项目(1101021B;2012M511678)

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。首先利用DE算法对Wiener模型参数进行辨识,分析了算法中变异率F对辨识过程中的全局并行搜索能力和收敛速度的影响;其次运用一种自适应变异差分进化算法(ADE)进行Wiener模型参数辨识,该算法在初期变异率较高,种群具有多样性,避免过早收敛于局部最优解;在进化过程中,变异率逐渐变小,优良个体得以保留,避免最优解遭到破坏。运用ADE算法对Wiener模型的数值仿真结果表明了ADE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。与一般的DE算法相比较,ADE算法辨识到全局最优解的精度和概率有较大提高,对算法参数的敏感性降低。DE algorithm is a population-based heuristic global search technology. The algorithm has simple principle, fewer control parameters, strong robustness, and good optimization performance. Firstly, differential evolution algorithm for parameters identification of Wiener model was used. The influence of mutation rate F on global parallel search ability and convergence in the process of identification were analyzed. Secondly, an adaptive differential evolution algorithm （ADE） was used to identify parameters of Wiener model. The algorithm keeps individual diversity to avoid premature convergence during the early stage and reduces the mutation rate gradually so as not to damage the optimal solution obtained during the later stage of the search process. Finally, numerical simulation was performed on Wiener model. The results show that ADE algorithm has more effectiveness in parameter identification problem than PSO. On the other hand, compared with the general DE algorithm, ADE algorithm identifies the parameters of Wiener model with higher precision as well as shows lower sensitivity to the al^zorithmic parameters.

关 键 词：差分进化算法 自适应变异 参数辨识 WIENER模型 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]