Fuzzy parameters estimation via hybrid methods

Year 2018, Volume: 47 Issue: 6, 1605 - 1624, 12.12.2018

Sedigheh Danesh

Abstract

Fuzzy regression analysis is one of the most widely used statistical techniques which represents the relation between variables. In this paper, the crisp inputs and the symmetrical triangular fuzzy output are considered. Two hybrid algorithms are considered to fit the fuzzy regression model. In this study, algorithms are based on adaptive neuro-fuzzy inference system. The results are derived based on the $V$-fold cross validation, so that the validity and quality of the suggested methods can be guaranteed. Finally, using the numerical examples, the performance of the suggested methods are compared with the other ones, such as linear programming (LP) and quadratic programming (QP). Based on examples, hybrid methods are verified for the prediction.

Keywords

Linear programming, quadratic programming, adaptive neuro-fuzzy inference system

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