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.<
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