一种新的TS模型辨识算法  

A Novel TS Model Identification Algorithm

作　　者：林妹娇[1] 陈水利[2] 

机构地区：[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

出　　处：《集美大学学报（自然科学版）》2013年第3期219-224,共6页Journal of Jimei University：Natural Science

基　　金：福建省科技厅产学研重大项目(2011H6020);福建省自然科学基金资助项目(2011J01013);厦门市科技计划项目(3502Z20123022)

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-Regressionmethod)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 Parti-cle 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.In this paper, a novel TS model identification algorithm is proposed. The identification algo-rithm is on the base of the following ideas： Firstly, the Mountain C-Regression method （MCR） is used to au- tomatically 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 val-ues 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.

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 

分 类 号：O29[理学—应用数学] P231[理学—数学]