Extraction of similarity based fuzzy rules from artificial neural networks
Metadatos
Afficher la notice complèteEditorial
Elsevier
Materia
Artificial neural networks Fuzzy systems Inteligencia artificial Artificial intelligence
Date
2006-05-03Referencia bibliográfica
C.J. Mantas, J.M. Puche, J.M. Mantas, Extraction of similarity based fuzzy rules from artificial neural networks, International Journal of Approximate Reasoning, Volume 43, Issue 2, 2006, Pages 202-221, ISSN 0888-613X, [https://doi.org/10.1016/j.ijar.2006.04.003]
Résumé
A method to extract a fuzzy rule based system from a trained artificial neural network for classification
is presented. The fuzzy system obtained is equivalent to the corresponding neural network.
In the antecedents of the fuzzy rules, it uses the similarity between the input datum and the weight
vectors. This implies rules highly understandable. Thus, both the fuzzy system and a simple analysis
of the weight vectors are enough to discern the hidden knowledge learnt by the neural network. Several
classification problems are presented to illustrate this method of knowledge discovery by using
artificial neural networks.