Various rule-extraction techniques using ANN have been used so far, most of them being applied on multi-layer ANN, since they are more easily handled. In many cases, extraction methods focusing on different types of networks and training have been implemented. However, there are virtually no methods that view the extraction of rules from ANN as systems which are independent from their architecture, training and internal distribution of weights, connections and activation functions. This paper proposes a ruleextraction system of ANN regardless of their architecture (multi-layer or recurrent), using Genetic Programming as a rule-exploration technique.
CITATION STYLE
Dorado, J., Rabuñal, J. R., Santos, A., Pazos, A., & Rivero, D. (2002). Automatic recurrent and feed-forward ANN rule and expression extraction with genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2439, pp. 485–494). Springer Verlag. https://doi.org/10.1007/3-540-45712-7_47
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