Fuzzy regression model with input and output Z-numbers

2020 ◽  
Vol 919 ◽  
pp. 052041
Author(s):  
Olga Poleshchuk
Keyword(s):  
Regression Model ◽  
Fuzzy Regression ◽  
Input And Output ◽  
Fuzzy Regression Model

scholarly journals FUZZY PRINCIPAL COMPONENT REGRESSION (FPCR) FOR FUZZY INPUT AND OUTPUT DATA

2006 ◽  
Vol 14 (01) ◽  
pp. 87-100 ◽  
Author(s):  
JIH-JENG HUANG ◽  
GWO-HSHIUNG TZENG ◽  
CHORNG-SHYONG ONG
Keyword(s):  
Principal Component Analysis ◽  
Regression Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Fuzzy Regression ◽  
Output Data ◽  
Input And Output ◽  
Fuzzy Input ◽  
Fuzzy Regression Model ◽  
Component Scores

Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollinearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regression model. In addition, a numerical example is used to demonstrate the proposed method and compare with other methods. On the basis of the results, we can conclude that the proposed method can provide a correct fuzzy regression model and avoid the problem of multicollinearity.

An Analysis of Poverty in Italy through a Fuzzy Regression Model

2011 ◽  
pp. 342-355 ◽  
Author(s):  
Silvestro Montrone ◽  
Francesco Campobasso ◽  
Paola Perchinunno ◽  
Annarita Fanizzi
Keyword(s):  
Regression Model ◽  
Fuzzy Regression ◽  
Fuzzy Regression Model

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.

Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy regression model

2017 ◽  
Vol 37 (2) ◽  
pp. 281-289 ◽  
Author(s):  
Narges Shafaei Bajestani ◽  
Ali Vahidian Kamyad ◽  
Ensieh Nasli Esfahani ◽  
Assef Zare
Keyword(s):  
Regression Model ◽  
Fuzzy Regression ◽  
Diabetic Patients ◽  
Fuzzy Regression Model
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