A robust least squares fuzzy regression model based on kernel function

In this paper, a new approach is presented to fit a robust fuzzy regression model based on some fuzzy quantities. In this approach, we first introduce a new distance between two fuzzy numbers using the kernel function, and then, based on the least squares method, the parameters of fuzzy regression m...

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Published in:Iranian journal of fuzzy systems (Online) 2020-07, Vol.17 (4), p.105
Main Authors: Khammar, A H, Arefi, M, Akbari, M G
Format: Article
Language:English
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Fuzzy sets
Numbers
Performance evaluation
Regression analysis
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Akbari, M G
description In this paper, a new approach is presented to fit a robust fuzzy regression model based on some fuzzy quantities. In this approach, we first introduce a new distance between two fuzzy numbers using the kernel function, and then, based on the least squares method, the parameters of fuzzy regression model is estimated. The proposed approach has a suitable performance to present the robust fuzzy model in the presence of different types of outliers. Using some simulated data sets and some real data sets, the application of the proposed approach in modeling some characteristics with outliers, is studied.
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subjects Fuzzy sets
Numbers
Performance evaluation
Regression analysis
title A robust least squares fuzzy regression model based on kernel function
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