基于自适应云粒子群算法的Wiener模型辨识

针对非线性系统Wiener模型的系统辨识问题,提出一种基于自适应云模型的粒子群优化(ACMPSO)算法的辨识方法。ACMPSO算法利用云模型实现优秀粒子的遗传和进化操作,根据进化状况动态调整云模型的参数,自适应地控制云模型算法的寻优范围和精度,有较强的全局搜索和局部求精能力。仿真实验证明该算法寻优精度高于其他主要PSO算法;将该算法应用于Wiener模型的系统辨识,通过实验证明了该辨识方法优于当前其他方法。

Identification of Wiener model based on adaptive cloud model particle swarm optimization algorithm

To identify the nonlinear system Wiener model, this paper put forward a new method based on adaptive cloud model particle swarm optimizationACMPSO algorithm, which introduced cloud model algorithm into the convergence process of PSO algorithm. The ACMPSO algorithm realized excellent particles' genetic and evolutional operation, adjusted cloud model's parameters according to evolutionary status and adaptive controled the search range and accuracy, therefore ACMPSO had better performance of global search and local optimization. The simulations prove the ACMPSO has better optimization performance than the other main PSOs. A numerical simulation of Wiener model is provided to prove the method has better identify performance than the other methods.