一种新的TS模型辨识算法

一种新的TS模型辨识算法

林妹娇1，陈水利2

（1.福州大学数学与计算机科学学院，福建福州 350108;2.集美大学理学院，福建厦

门 361021）

［摘要］提出一种新的TS模型辨识算法.该算法思想：首先采用MCR算法（Mountain C Regression method）自动确定聚类数目和初始聚类中心，然后采用改进的GK （Gustafon Kessl）聚类算法得到最优的划分矩阵，再根据最优划分矩阵计算系统前件参数的最优值，最后用自适应粒子群优化算法（Adaptive Particle Swarm Optimization,APSO）对后件参数进行优化.此辨识算法能够用较少的规则数描述给定的未知系统，并且容易实现.仿真实验表明该算法能够实现非线性系统的辨识，并且可获得相对高的精度.

[关键词]TS模型辨识；MCR算法；改进的GK聚类算法；自适应粒子群优化算法

A Novel TS Model Identification Algorithm

LIN Mei-jiao1，CHEN Shui-li2

(1.College of Mathematics and Computer Science，Fuzhou University，Fuzhou 350108，China；2.School of Science，Jimei University，Xiamen 361021，China)

Abstract：In this paper，a novel TS model identification algorithm is proposed.The identification algorithm is on the base of the following ideas：Firstly，the Mountain C-Regression method (MCR) is used to automatically identify the number of clusters and initial cluster center.Secondly，the modified Gustafson-Kessl (GK) algorithm is used to obtain an optimal input-output space fuzzy partition matrix which provids the values of premise parameters.Finally，Adaptive Particle Swarm Optimization (APSO) algorithm is adopted to precisely adjust consequent parameters.It can express a given unknown system with a small number of fuzzy rules and is easy to

implement.The simulation results show the proposed algorithm realizes the identification of the nonlinear system with relative high accuracy.

Key words：Takagi Sugeno model identification；Mountain C-Regression method,MCR；modified GK algorithm；Adaptive Particle Swarm Optimization,APSO