T-S Fuzzy Affine Linear Modeling Algorithm by Possibilistic c-Regression Models Clustering Algorithm

被引：0
|
作者：
Kung, Chung-Chun [1 ]
Ku, Hong-Chi [1 ]
机构：
[1] Tatung Univ, Dept Elect Engn, Taipei 104, Taiwan
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2014年
关键词：
Takagi-Sugeno (T-S) fuzzy model; affine linear; possibilistic c-regression models (PCRM); cluster validity criterion; IDENTIFICATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a Takagi-Sugeno (T-S) fuzzy affine linear modeling algorithm by the possibilistic c-regression models (PCRM) clustering algorithm. We apply the PCRM to partition the given input-output data into hyper-plane-shaped clusters (regression models). We choose the suitable number of cluster by the cluster validity criterion and then to construct the T-S fuzzy affine linear model. A simulation example is provided to demonstrate the effectiveness of the T-S fuzzy affine linear modeling algorithm.
引用
收藏
页码：1242 / 1247
页数：6
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