Abstract
This paper proposes a new method for the extraction of knowledge from a trained type feed-forward neural network. The new knowledge extracted is expressed by fuzzy rules directly from a sensibility analysis between the inputs and outputs of the relationship that model the neural network. This easy method of extraction is based on the similarity of a fuzzy set with the derivative of the tangent hyperbolic function used as an activation function in the hidden layer of the neural network. The analysis performed is very useful, not only for the extraction of knowledge, but also to know the importance of every rule extracted in the whole knowledge and, furthermore, the importance of every input stimulating the neural network.
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© 2002 Springer-Verlag Berlin Heidelberg
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Besada-Juez, J.M., Sanz-Bobi, M.A. (2002). Extraction of Fuzzy Rules Using Sensibility Analysis in a Neural Network. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_64
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DOI: https://doi.org/10.1007/3-540-46084-5_64
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