T–S Fuzzy Model Identification Based on a Novel Hyperplane-Shaped Membership Function

The fuzzy membership function is a crucial factor that may affect the model structure and modeling accuracy. Although hyperplane-shaped clustering (HPSC) has been widely used in Takagi-Sugeno (T-S) fuzzy model identification, there is no well-designed membership function in these approaches. The com...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on fuzzy systems Vol. 25; no. 5; pp. 1364 - 1370
Main Authors Li, Chaoshun, Zhou, Jianzhong, Chang, Li, Huang, Zhengjun, Zhang, Yongchuan
Format Journal Article
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Clustering algorithms
Clustering methods
Data models
Fuzzy c-regressive model
fuzzy membership function
Fuzzy sets
hyperplane-shaped Gaussian function
Linear regression
Parameter estimation
Partitioning algorithms
Takagi–Sugeno (T–S) fuzzy model
Online AccessGet full text

Cover

More Information
Summary:The fuzzy membership function is a crucial factor that may affect the model structure and modeling accuracy. Although hyperplane-shaped clustering (HPSC) has been widely used in Takagi-Sugeno (T-S) fuzzy model identification, there is no well-designed membership function in these approaches. The commonly used bell-shaped Gaussian function is actually inappropriate to employ together with HPSC in fuzzy modeling. In this paper, a hyperplane-shaped fuzzy membership function is designed to match HPSC for T-S fuzzy model identification for the first time. Experimental results on several benchmark problems indicate that modeling accuracies have been greatly improved from the original modeling approach by replacing the proposed membership function.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2016.2598850