Learning by fuzzified neural networks

Abstract

We derive a general learning algorithm for training a fuzzified feedforward neural networks that has fuzzy inputs, fuzzy targets, and fuzzy conncetion weights. The derived algorithm is applicable to the learning of fuzzy connection weights with various shapes such as triangular and trapezoid. First we briefly describe how a feedforward neural network can be fuzzified. Inputs, targets, and connection weights in the fiuzzified neural network can be fuzzy numbers. Next we define a cost function that measures the differences between a fuzzy target vector and an actual fuzzy output vector. Then we derive a learning algorithm from the cost function for adjusting fuzzy connection weights. Finally we show some results of computer simulations.