T–S Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm

2012 ◽  
Vol 20 (2) ◽  
pp. 305-317 ◽  
Author(s):  
Chaoshun Li ◽  
Jianzhong Zhou ◽  
Bo Fu ◽  
Pangao Kou ◽  
Jian Xiao
Keyword(s):  
Clustering Algorithm ◽  
Model Identification ◽  
Fuzzy Model ◽  
Hyperplane Clustering ◽  
Gravitational Search

T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

2009 ◽  
Vol 22 (4-5) ◽  
pp. 646-653 ◽  
Author(s):  
Chaoshun Li ◽  
Jianzhong Zhou ◽  
Xiuqiao Xiang ◽  
Qingqing Li ◽  
Xueli An
Keyword(s):  
Regression Model ◽  
Clustering Algorithm ◽  
Model Identification ◽  
Fuzzy Model

A clustering algorithm for fuzzy model identification

1998 ◽  
Vol 98 (3) ◽  
pp. 319-329 ◽  
Author(s):  
Jian-Qin Chen ◽  
Yu-Geng Xi ◽  
Zhong-Jun Zhang
Keyword(s):  
Clustering Algorithm ◽  
Model Identification ◽  
Fuzzy Model

Fuzzy Model Identification With Enhanced Validity Criterion for Mechanical System Design

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Kuo-Ho Su
Keyword(s):  
System Design ◽  
Mechanical System ◽  
Clustering Algorithm ◽  
Structural Information ◽  
Model Identification ◽  
Fuzzy Model ◽  
Optimal Number ◽  
Identification Algorithm ◽  
Mathematical Tool ◽  
Validity Criterion

Model identification for machine system design, design optimization, and manufacturing planning is an important method that has high prediction accuracy and could become an essential stage in practical applications. In this paper, an effective fuzzy model identification algorithm for mechanical system design is developed. First, a fuzzy c-regression model clustering algorithm, in which hyperplane-shaped cluster representatives are utilized to provide a mathematical tool to partition the input–output space reasonably, is introduced. Then, an enhanced cluster validity criterion, in which the structural information hidden in the clusters can be reflected in the index, is proposed to choose the optimal number of clusters. In the proposed architecture, an improved Takagi–Sugeno fuzzy model is proposed to describe the system. Two illustrative examples under various conditions are provided, and their performances are indicated in comparison with other published works. In comparison to these fuzzy works, the proposed fuzzy model identification requires fewer fuzzy rules and a shorter tuning time.

Sign in / Sign up

Export Citation Format

Share Document