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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Fukumi, M., Mitsukura, Y., Akamatsu, N. (2001). Knowledge Incorporation and Rule Extraction in Neural Networks. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_174
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DOI: https://doi.org/10.1007/3-540-44668-0_174
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