A Novel Type-2 Fuzzy Identification Method Based on the Improved Membership Function

被引：3
|
作者：
Tsai, Shun-Hung [1 ,2 ]
Wu, Cheng-Yun [2 ]
Chen, Yan-He [1 ]
机构：
[1] Natl Sun Yat sen Univ, Dept Elect Engn, Kaohsiung, Taiwan
[2] Natl Taipei Univ Technol, Grad Inst Automat Technol, Taipei, Taiwan
来源：
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS | 2023年 / 25卷 / 05期
关键词：
Takagi-Sugeno (T-S) fuzzy model; System identification; Membership function; Fuzzy C-regression model; Interval type-2 fuzzy sets; SYSTEMS IDENTIFICATION; NEURAL-NETWORK; REGRESSION; PREDICTION;
D O I：
10.1007/s40815-023-01494-1
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a type-2 fuzzy identification method based on the improved membership function is proposed. First, in order to improve the effect of the noise or outliers for the identification process, an objective function involved the ratio of identification error based on the natural logarithm is proposed to reduce the effect of noise or outliers for the system identification process. In addition, based on the principal component analysis and Mahalanobis distance, the membership function is modified to improve the weight ratio and the orthogonal least square method is adopted to modify the consequent parameters. Lastly, some experiments are illustrated to show that the proposed results can provide the less identification error than some existing studies.
引用
收藏
页码：1818 / 1833
页数：16
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