Non-linear Based Fuzzy Random Regression for Independent Variable Selection

This paper demonstrates a fuzzy random regression approach using genetic algorithm (FRR-GA) to select independent variable for regression model. The FRR-GA approach enables us to indicate the best coefficient values among regressor that indicate the best independent variable, which is important to build regression model. Additionally, the fuzzy random regression approach is employed to treat dual uncertainties due to the realization of such data in real application. This paper presents an algorithm reflecting the non-linear strategy in the fuzzy random regression model. A numerical example illustrates the proposed solution procedure whereby the result suggested several feasible solutions to the user.

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