Quadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection. (21st August 2019)

• Record Type: Journal Article
• Authors: Gao, Pingping; Gao, Yabin
• Other Names: Szekrenyes Andras Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-08-21
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/4914593
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding the data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the sense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the data outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression model. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is designed for detecting data outlier. An example is finally provided to validate the presented methods.