T-S fuzzy model identification based on an improved interval type-2 fuzzy c-regression model

• Published in: Journal of intelligent & fuzzy systems Vol. 44; no. 3; pp. 4495 - 4507
• Main Author: Shi, Jianzhong
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2023; Sage Publications Ltd
• Subjects: Algorithms; Clustering; Fluidized beds; Hyperplanes; Nonlinear systems; Outliers (statistics); Regression models; Fuzzy identification; interval type-2 fuzzy c-regression model; T-S fuzzy model; fuzzy clustering; orthogonal least squares
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-221434

Fuzzy clustering has been widely applied in T-S fuzzy model identification for nonlinear systems, however, tradition type-1 fuzzy clustering algorithms can’t deal with uncertainties in real world, an improved interval type-2 fuzzy c-regression model (IT2-FCRM) clustering is proposed for T-S fuzzy model identification in this paper. The improved IT2-FCRM adapts a new objective function, which makes the boundary of clustering more clearly and reduces the influence of outliers or noisy data on clustering results. The premise parameters of T-S fuzzy model are upper and lower hyperplanes obtained by improved IT2-FCRM, and the upper and lower hyperplanes are used to build hyper-plane-shaped type-2 Gaussian membership function. Compared with the hyper-sphere-shaped membership function of tradition IT2-FCRM, the hyper-plane-shaped membership function is more coincided with point to plane sample distance described by FCRM clustering. The simulation results of several benchmark problems and a real bed temperature in circulating fluidized bed plant show that the identification algorithm has higher accuracy.