Extraction of similarity based fuzzy rules from artificial neural networks Mantas Ruiz, Carlos Javier Puche, J. M. Mantas Ruiz, José Miguel Artificial neural networks Fuzzy systems Inteligencia artificial Artificial intelligence 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. 2022-11-10T12:33:58Z 2022-11-10T12:33:58Z 2006-05-03 info:eu-repo/semantics/article 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] https://hdl.handle.net/10481/77893 10.1016/j.ijar.2006.04.003 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier