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

Article type: Research Article

Authors: Shi, Jianzhong^{; *}

Affiliations: School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, China

Correspondence: [*] Corresponding authors. Jianzhong Shi, School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, China. E-mail: sjz@njit.edu.cn.

Abstract: 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.

Keywords: Fuzzy identification, interval type-2 fuzzy c-regression model, fuzzy clustering, T-S fuzzy model, orthogonal least squares

DOI: 10.3233/JIFS-221434

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4495-4507, 2023