A novel identification method for Takagi-Sugeno fuzzy model

被引：43

作者：

Tsai, Shun-Hung ^{[1
]}

Chen, Yu-Wen ^{[1
]}

机构：

[1] Natl Taipei Univ Technol, Grad Inst Automat Technol, Taipei, Taiwan

来源：

FUZZY SETS AND SYSTEMS | 2018年 / 338卷

关键词：

Takagi-Sugeno fuzzy model; Fuzzy c-means; Particle swarm optimization; Fuzzy c-regression model; C-REGRESSION MODELS; SYSTEMS IDENTIFICATION; SPARSE REPRESENTATION; ALGORITHM; STABILIZATION; STABILITY; DESIGN; ANFIS;

D O I：

10.1016/j.fss.2017.10.012

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Based on the Xie-Beni index and an improved particle swarm optimization algorithm, a novel identification method for the Takagi-Sugeno fuzzy model is proposed in this paper. Firstly, Xie-Beni indices with a fuzzy c-means clustering algorithm are adopted to find the rule number of the Takagi-Sugeno fuzzy model. By utilizing the particle swarm optimization algorithm, the initial membership function and the consequent parameters of the fuzzy model are obtained. In addition, through an improved fuzzy c-regression model and orthogonal least-square method, the premise structure and consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some well-known models are used to demonstrate that the proposed method outperforms some existing methods. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：117 / 135

页数：19

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