Abstract
Usually, fuzzy rules contain in the antecedent propositions that restrict a variable to a fuzzy value by means of an equal-to predicate. We propose to improve the interpretability of fuzzy models by extending the syntax of their rules. With this aim, on one hand, new predicates are considered in the rule antecedents and, on the other hand, rules can be associated with exceptions that modify the output of those rules in a region of their covered input space. The method stems from an initial fuzzy model described with the usual fuzzy rules and uses an ACO algorithm to search the optimal set of extended rules that describes this model.
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Carmona, P., Castro, J.L. (2007). An Ant Colony Optimization plug-in to Enhance the Interpretability of Fuzzy Rule Bases with Exceptions. In: Melin, P., Castillo, O., RamÃrez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_44
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DOI: https://doi.org/10.1007/978-3-540-72432-2_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72431-5
Online ISBN: 978-3-540-72432-2
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