New system identification technique using fuzzy regression analysis

2002 ◽  
Author(s):  
M. Kaneyoshi ◽  
H. Tanaka ◽  
M. Kamei ◽  
H. Furuta
Keyword(s):  
Regression Analysis ◽  
System Identification ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis ◽  
Identification Technique ◽  
New System

Robust fuzzy regression analysis

2011 ◽  
Vol 181 (19) ◽  
pp. 4154-4174 ◽  
Author(s):  
Pierpaolo D’Urso ◽  
Riccardo Massari ◽  
Adriana Santoro
Keyword(s):  
Regression Analysis ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis

scholarly journals Quadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Pingping Gao ◽  
Yabin Gao
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Outlier Detection ◽  
Euclidean Distance ◽  
Fuzzy Numbers ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis ◽  
Fuzzy Regression Model ◽  
Interval Type

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.

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