2020-12-152020-12-152012Kahraman, U. M., Evren, A. (2012). Artıfıcıal Neural Network and Entropy Approach ın Fuzzy Nonlınear Regressıon. Journal of Selcuk University Natural and Applied Science, 1, (1), 14-29.2147-3781https://hdl.handle.net/20.500.12395/40782Fuzzy nonlinear regression (FNR) is different from classic regression models just because its output consists of fuzzy numbers. Predictions are realized by FNR models for the cases in which both input variables are nonlinearly related and output variable is fuzzy. Besides, a FNR model may be used to construct a probability interval for the output variable precisely. It is important to note that an entropy-based approach to FNR models results in smaller propagations for fuzzy intervals.Fuzzy nonlinear regression (FNR) is different from classic regression models just because its output consists of fuzzy numbers. Predictions are realized by FNR models for the cases in which both input variables are nonlinearly related and output variable is fuzzy. Besides, a FNR model may be used to construct a probability interval for the output variable precisely. It is important to note that an entropy-based approach to FNR models results in smaller propagations for fuzzy intervals.trinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/openAccessNonlinear regressionNonlinear regressionneural networksneural networksfuzzy set theoryfuzzy set theoryentropy approachentropy approachArticle111429