Knowledge Incorporation and Rule Extraction in Neural Networks

Authors:
Minoru Fukumi;Yasue Mitsukura;Norio Akamatsu
Affiliations:
-;-;-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

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Abstract

In this paper a new knowledge incorporation and rule extraction method in neural networks is presented. The rule form of an if-then type can be inserted into a neural network (NN) as knowledge of a problem. NN is then trained by using a set of training samples. In this case the structure learning algorithm with forgetting is used to generate a small-sized NN system. After the NN training, rules are extracted from it. The results of computer simulations show that this approach can generate obvious network architectures and as a result simple rules compared with conventional rule extraction methods.