Fuzzy regression by fuzzy number neural networks

Authors:
James P. Dunyak;Donald Wunsch
Affiliations:
Department of Mathematics, Texas Tech University, Lubbock, TX 79409, USA;Department of Electrical Engineering, Texas Tech University, Lubbock, TX 79409, USA
Venue:
Fuzzy Sets and Systems
Year:
2000

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Abstract

In this paper, we describe a method for nonlinear fuzzy regression using neural network models. In earlier work, strong assumptions were made on the form of the fuzzy number parameters: symmetric triangular, asymmetric triangular, quadratic, trapezoidal, and so on. Our goal here is to substantially generalize both linear and nonlinear fuzzy regression using models with general fuzzy number inputs, weights, biases, and outputs. This is accomplished through a special training technique for fuzzy number neural networks. The technique is demonstrated with data from an industrial quality control problem.