Adapted gradient algorithm for algebraic fuzzy neural networks

Abstract

A learning algorithm based on a gradient technique is introduced for the algebraic fuzzy neural network with fuzzy weights. The fuzzy weights can be triangular fuzzy numbers (usually nonsymmetric), or trapezoidal fuzzy numbers. The network is able to map a vector of triangular (trapezoidal) fuzzy numbers into any other vector of triangular (trapezoidal) fuzzy numbers.