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.
出处
《集美大学学报（自然科学版）》
CAS
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)
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
Takagi-Sugeno model identification
Mountain C-Regression method
MCR
modified GK algorithm
Adaptive Particle Swarm Optimization
APSO
