A linear regression model for nonlinear fuzzy data

Authors:
Juan Carlos Figueroa-Garcí/a;Jesus Rodriguez-Lopez
Affiliations:
Universidad Distrital Francisco José/ de Caldas, Bogot$#225/, Colombia;Universidad Distrital Francisco José/ de Caldas, Bogot$#225/, Colombia
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

Fuzzy linear regression is an interesting tool for handling uncertain data samples as an alternative to a probabilistic approach. This paper sets forth uses a linear regression model for fuzzy variables; the model is optimized through convex methods. A fuzzy linear programming model has been designed to solve the problem with nonlinear fuzzy data by combining the fuzzy arithmetic theory with convex optimization methods. Two examples are solved through different approaches followed by a goodness of fit statistical analysis based on the measurement of the residuals of the model.