Recurrent algebraic fuzzy neural networks based on fuzzy numbers

Abstract

A hybrid structure, recurrent algebraic fuzzy neural networks (RAFNN) using fully connected recurrent neural network architecture is proposed. The hybrid structure is based on neural network topology and fuzzy algebraic systems. All the operations are defined in the frame of fuzzy arithmetic using triangular fizzy numbers (usually non-symmetric). The experimental results demonstrate the capability of algorithm and the possibility to use successfully fuzzy numbers in recurrent architecture in order to acquire a dynamic behavior.