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An interval-based approach to fuzzy regression for fuzzy input-output data

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Jalal Chachi; S. Mahmoud Taheri; H. Rezaei Pazhand; Maharab Geotechnical
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Abstract:

A novel approach is introduced to construct a fuzzy regression model when the data available of independent and dependent variables are fuzzy numbers. The approach, consi...Show More

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Abstract:

A novel approach is introduced to construct a fuzzy regression model when the data available of independent and dependent variables are fuzzy numbers. The approach, consisting on the least-squares method, uses the α-level sets of fuzzy observations to estimate the crisp parameters of the model. A competitive study shows the performance and efficiency of the proposed approach with respect to some well-known methods.
Published in: 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)
Date of Conference: 27-30 June 2011
Date Added to IEEE Xplore: 01 September 2011
ISBN Information:

ISSN Information:

DOI: 10.1109/FUZZY.2011.6007457
Conference Location: Taipei, Taiwan
References is not available for this document.
Contents

I. Introduction

Identification and analysis of the functional relationship between a dependent and some independent variables have made great interest in statistical analysis. Based on such functional relationship, which is called regression model, one can describe and predict the values of the dependent variable using the observations of independent variables.

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