摘要
为解决复杂非线性系统的辨识问题,提出了一种基于进化粒子群优化算法的非线性系统辨识方法。在标准粒子群优化算法的基础上引入一种进化策略,增加粒子的多样性。在算法迭代寻优的过程中,通过对群体中的粒子进行选择、变异等进化操作,构造进化粒子群优化算法,提高算法的全局搜索能力。将非线性系统辨识问题转化为非线性连续域优化问题,利用进化粒子群优化算法进行并行、高效搜索,以获得该优化问题的解。通过对多输入单输出的Wiener-Hammerstein模型进行辨识,验证了该方法的正确性和可行性。
Nonlinear system identification is one of the most important topics of modern identification.A novel approach for complex nonlinear system identification is proposed based on evolution particle swarm optimization（EPSO） algorithm.In order to increase the diversity of particle,a new evolutionary strategy in the standard particle swarm optimization（PSO） algorithm is introduced.Firstly,in the iterations of algorithm optimization process,Evolution of PSO algorithm is constructed to improve the capacity of global search algorithms by controlling groups of particles in the selection,variation,such as evolutionary operation.Secondly,the problems of nonlinear system identification are converted to nonlinear optimization problems in continual space,and then the EPSO algorithm is used to search the parameter concurrently and efficiently to find the optimal estimation of the system parameters.The feasibility of the proposed method is demonstrated by the identification of a multi-input and single-output Wiener-Hammerstein model.
出处
《计算机仿真》
CSCD
北大核心
2010年第10期179-182,186,共5页
Computer Simulation
关键词
进化粒子群
系统辨识
非线性系统
Evolution particle swarm optimization
System identification
Nonlinear system
