基于混合优化算法的模糊系统辨识  

Identification of Hierarchical Fuzzy System Based on Hybrid Optimization Algorithm

作　　者：朱晓东[1] 刘丹[1] 李广[1] 

机构地区：[1]郑州大学电气工程学院,河南郑州450001

出　　处：《郑州大学学报（工学版）》2015年第4期10-14,共5页Journal of Zhengzhou University（Engineering Science）

基　　金：国家自然科学基金资助项目(60905039)

摘　　要：针对一种新型分层模糊系统,提出了一种混合优化算法,即利用粒子群优化算法辨识每一个模糊单元模型的前件参数,利用递推最小二乘算法辨识后件参数.采用该辨识方法对Mackey-Glass混沌时间序列及Box-Jenkins数据进行实验,并与果蝇优化算法以及入侵杂草优化算法的仿真结果进行了比较,实验结果表明:这种混合优化算法能够提高分层模糊系统模型的精度.In view of a new type of hierarchical fuzzy system,a hybrid optimization algorithm is proposed in this paper. The antecedent parameters of each fuzzy unit model are estimated by the particle swarm optimization algorithm,and the recursive least square algorithm is used to determine the parameters of consequents.Experiments on the well-known Box-Jenkins set data and the chaotic Mackey-Glass time series are carried out.The proposed hybrid optimization algorithm is compared with the fruit fly optimization algorithm and the invasive weed optimization algorithm. The result of experiments shows that the hybrid optimization algorithm can improve the accuracy of the hierarchical fuzzy system model.

关 键 词：分层模糊系统 粒子群优化算法 递推最小二乘法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]