基于自适应粒子群算法的非线性系统模糊辨识

Nonlinear system fuzzy identification based on adaptive particle swarm optimization

针对传统方法的非线性系统模糊辨识精准度较低,且辨识速度较慢的问题,该文提出一种基于自适应粒子群算法的非线性系统模糊辨识方法。通过非线性系统模型原理,估计实质模型参数,从而将问题转换成非线性函数优化,获得模型参数估计值。引入自适应粒子群算法,并结合核函数的FCM聚类方法,清除在辨识非线性系统的不利行为,即可完成非线性系统的模型辨识。通过实验证明,该文方法能够有效地对非线性系统模糊辨识,且与传统方法对比,该文方法的辨识精度较高,速度更快。

In allusion to the problem of low accuracy and slow identification speed of the fuzzy identification of the nonlinear system with traditional method,a nonlinear system fuzzy identification method based on adaptive particle swarm optimization algorithm is proposed.The real model parameters are estimated by means of the principle of nonlinear system model,thus the problem is transformed into nonlinear function optimization to obtain the estimate of the model parameters.The adaptive particle swarm is introduced and combined with the FCM(fuzzy c⁃means algorithm)clustering method of the kernel function to eliminate the disadvantageous behavior in identifying nonlinear systems,so as to complete the model identification of the nonlinear system.The experiments show that this method can effectively identify nonlinear systems,and in comparison with the traditional method,this method has higher identification accuracy and faster identification speed.