An architecture of neural networks for input vectors of fuzzy numbers

Abstract

The authors proposed an architecture of multilayer feedforward neural networks for classification problems of fuzzy vectors. A fuzzy input vector is mapped to a fuzzy number by the proposed neural network where the activation function is extended to a fuzzy input-output relation by the extension principle. A learning algorithm is derived from a cost function defined by a target output and the level set of a fuzzy output. The proposed classification method of fuzzy vectors is illustrated by a numerical example.<>