基于APSO算法的参数辨识与优化

Parameter Identification and Optimization Based on Adaptive Particle Swarm Optimization Algorithm

将自适应粒子群优化(APSO)算法应用在系统辨识和参数优化中,定性地分析系统参数空间范围,把系统辨识和参数优化问题转化为参数空间寻优,利用APSO算法在寻优过程中有效避免局部最优的特点,在整个参数空间内并行寻找获得系统参数的最优解。通过对多种模型的仿真实验研究表明,APSO算法在系统辨识和参数优化问题中优于原有的GA和PSO方法。

The adaptive particle swarm optimization （APSO） algorithm is used in system identification and parameter optimization. The problems of system identification and parameter optimization can be viewed as optimization problems in parameter space by qualitatively analyze the scope of system parameter space. The adaptive parti- cle swarm optimization algorithm can effectively avoid getting into local optimum and is used to obtain the optimal solution by searching in the whole parameter space in parallel. The simulations were done for different model exam- pies. The experiment results show that adaptive particle swarm optimization algorithm is an effective method better than GA and PSO for system identification and parameter optimization.