Rule extraction: using neural networks or for neural networks?
Journal of Computer Science and Technology
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Fuzzy Sets and Systems
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Engineering Applications of Artificial Intelligence
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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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IEEE Transactions on Fuzzy Systems - Special section on computing with words
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IEEE Transactions on Neural Networks
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Fault fuzzy rule extraction from AC motors by neuro-fuzzy models
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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This paper presents an extension of the method presented by Benitez et al (1997) for extracting fuzzy rules from an artificial neural network (ANN) that express exactly its behavior. The extraction process provides an interpretation of the ANN in terms of fuzzy rules. The fuzzy rules presented are in accordance with the domain of the input variables. These rules use a new operator in the antecedent. The properties and intuitive meaning of this operator are studied. Next, the role of the biases in the fuzzy rule-based systems is analyzed. Several examples are presented to comment on the obtained fuzzy rule-based systems. Finally, the interpretation of ANNs with two or more hidden layers is also studied