Fuzzy radial basis function network for fuzzy regression with fuzzy input and fuzzy output

• Published in: Complex & intelligent systems 2016-03, Vol.2 (1), p.61-73
• Main Authors: Pehlivan, Nimet Yapıcı, Apaydın, Ayşen
• Format: Article
• Language: English
• Subjects: Algorithms; Back propagation; Complexity; Computational Intelligence; Cost function; Data Structures and Information Theory; Engineering; Linear programming; Original Article; Radial basis function
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-016-0013-9

In this study, fuzzy regression (FR) models with fuzzy inputs and outputs are discussed. Some of the FR methods based on linear programming and fuzzy least squares in the literature are explained. Within this study, we propose a Fuzzy Radial Basis Function (FRBF) Network to obtain the estimations for FR model in the case that inputs and outputs are symmetric/nonsymmetric triangular fuzzy numbers. Proposed FRBF Network approach is a fuzzification of the inputs, outputs and weights of traditional RBF Network and it can be used as an alternative to FR methods. The FRBF Network approach is constructed on the basis of minimizing the square of the total difference between observed and estimated outputs. A simple training algorithm from the cost function of the FRBF Network through Backpropagation algorithm is developed in this study. The advantage of our proposed approach is its simplicity and easy computation as well as its performance. To compare the performance of the proposed method with those given in the literature, three numerical examples are presented.