Abstract
This paper focuses on problems related to learning rules using numerical data for the Hierarchical Fuzzy Logic Systems (HFLS) described in [9]. Learning rules for Fuzzy Logic Systems (FLS) or Fuzzy Controller (FC) in short could be accomplished by using many different approaches, building one, complex rulebase using all available input and output variables. Using hierarchical structure we could avoid this problem by problem division into subproblems with smaller dimensions. ”Hierarchical” means that fuzzy sets produced as output of one of fuzzy controllers are then processed as an inputs of another one as the sets of auxiliary variables. The main problem is to learn rulebase with numerical data, which does not contain any data for those auxiliary variables. The main scope of this paper is to present an algorithm that is being a solution to this problem and provides support for selective activation of unit FC. The proposal presented in this paper operates on a type-1 HFLS, built with the fuzzy controllers (in the sense of Mamdani). An example of single-player games, i.e. where the “enemy” is controlled by agents is used.
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Renkas, K., Niewiadomski, A., Kacprowicz, M. (2015). Learning Rules for Hierarchical Fuzzy Logic Systems with Selective Fuzzy Controller Activation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_24
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DOI: https://doi.org/10.1007/978-3-319-19324-3_24
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