Abstract
Weighted fuzzy logic programs extend the expressiveness of fuzzy logic programs by allowing the association of a different significance weight with each atom that appears in the body of a fuzzy rule. The semantics and a connectionist representation of these programs have already been studied in the absence of negation; in this paper we first propose a Kripke-Kleene based semantics for the programs which allows for the use of negation as failure. Taking advantage of the increased modelling capabilities of the extended programs, we then describe their connectionist representation and study the problem of adapting the rule weights in order to fit a provided dataset. The adaptation algorithm we develop is based on the subgradient descent method and hence is appropriate to be employed as a learning algorithm for the training of the connectionist representation of the programs.
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References
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Chortaras, A., Stamou, G., Stafylopatis, A., Kollias, S. (2008). Adaptation of Connectionist Weighted Fuzzy Logic Programs with Kripke-Kleene Semantics. In: Kůrková, V., Neruda, R., KoutnÃk, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_51
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DOI: https://doi.org/10.1007/978-3-540-87536-9_51
Publisher Name: Springer, Berlin, Heidelberg
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