Fuzzy rule extraction from a multilayered neural network

A fuzzy rule extraction method from a multilayered neural network is proposed to realize the multiplicative merits of neural and fuzzy theories. The proposed method uses a fuzzy-neuron network structure which includes input and output layers that convert input signals to membership values. An evaluation index, called the `casual index´ can evaluate the weights which are acquired by learning and can translate the internal knowledge of the network into fuzzy rules. Simulations using test data whose relationships are known a priori demonstrated the ability of the proposed method to extract fuzzy rules properly from a multilayered neural network