A gradient descent learning algorithm for fuzzy neural networks
Thomas Feuring, James J. Buckley, Yoichi Hayashi · 2002
In order to train fuzzy neural nets fuzzy number weights have to be adjusted. Since fuzzy arithmetic automatically leads to monotonic increasing outputs a direct fuzzification of the backpropagation method does not work. Therefore, other strategies like evolutionary algorithms are being considered in the literature. In this paper we suggest a backpropagation based method of adjusting the weights. Furthermore, we show that by using the proposed method convergence can be guaranteed.