Abstract
Imperfect methods of aquiring knowledge from experts in order to create fuzzy rules are generally known [16,4,25]. Since this is a very important part of fuzzy inference systems, this article focuses on presenting new learning methods for fuzzy rules. Referring to earlier work, the authors extended learning methods for fuzzy rules on applications of Type-2 fuzzy logic systems to control filters reducing air pollution. The filters use Selective Catalytic Reduction (SCR) method and, as for now, this process is controlled manually by a human expert.
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Kacprowicz, M., Niewiadomski, A., Renkas, K. (2015). Learning Rules for Type-2 Fuzzy Logic System in the Control of DeNOx Filter. 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_17
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DOI: https://doi.org/10.1007/978-3-319-19324-3_17
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