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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References
Casillas, J., Cordon, O., Herrera, F.: Improving the wang and mendel’s fuzzy rule learning method by inducing cooperation among rules (2000)
Christian, R.A., Lad, R.K., Deshpande, A.W., Desai, N.G.: Fuzzy MCDM approach for addressing composite index of water and air pollution potential of industries. International Journal of Digital Content Technology and its Applications 1, 4–71 (2008)
Cirstea, M.N.: Neural and fuzzy logic control of drives and power systems. Newnes (2002)
Cordon, O., Herrera, F., Villar, P.: Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Transactions on Fuzzy Systems 9(4), 667–674 (2001)
Gegov, A.E., Frank, P.M.: Hierarchical fuzzy control of multivariable systems. Fuzzy Sets and Systems 72, 299–310 (1995)
Hammell, R., Sudkamp, T.: Learning fuzzy rules from data. In: The Application of INformation Technologies (Computer Science) to Mission Systems (1998)
Kacprowicz, M., Niewiadomski, A.: On dedicated fuzzy logic systems for emission control of industrial gases. In: Trends in Logic XIII (2014)
Karnik, N.N., Mendel, J.M.: Centroid of a type-2 fuzzy set. Information Sciences 132, 195–220 (2001)
Kuropka, J.: The test with ammonia nitrogen oxide reduction catalysts granular (in Polish, Badanie redukcji tlenkw azotu amoniakiem na katalizatorach ziarnistych). Ochrona rodowiska pp. 15–18 (1994)
Liang, Q., Mendel, J.M.: Interval type-2 fuzzy logic systems: Theory and design. IEEE Transactions on Fuzzy Systems 8, 535–550 (2000)
Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall (2001)
Niewiadomski, A., Kacprowicz, M.: Higher order fuzzy logic in controlling selective catalytic reduction systems. Bulletin of the Polish Academy of Sciences Technical Sciences 62(4), 743–750 (2014)
Renkas, K., Niewiadomski, A.: Hierarchical fuzzy logic systems: Current research and perspectives. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 295–306. Springer, Heidelberg (2014)
Rutkowska, D., Pilinski, M., Rutkowski, L.: Neural networks, genetic algorithms and fuzzy systems (in Polish, Sieci neuronowe, algorytmy genetyczne i systemy rozmyte). Scientific Publishing PWN, Warsaw-Lodz (1997)
Rutkowski, L.: Methods and techniques of artificial intelligence (in Polish, Metody i techniki sztucznej inteligencji). Scientific Publishing PWN, Warsaw (2009)
Serrurier, M., Sudkamp, T., Dubois, D., Prade, H.: Fuzzy inductive logic programming: Learning fuzzy rules with their implication. In: The 14th IEEE International Conference on Fuzzy Systems, FUZZ 2005, pp. 613–618 (2005)
Shahmaleki, P., Mahzoon, M.: Designing a hierarchical fuzzy controller for backing-up a four wheel autonomous robot. Proceedings of the American Control Conference (ACC 2008) (FrB17.5), June 11-13, pp. 4893–4897 (2008)
Smoczek, J.: Interval arithmetic-based fuzzy discrete-time crane control scheme design. Bulletin of the Polish Academy of Sciences Technical Sciences 61(4), 863–870 (2013)
Starczewski, J.T.: Extended triangular norms on gaussian fuzzy sets. In: Montseny, E., Sobrevilla, P. (eds.) EUSFLAT Conf., pp. 872–877. Universidad Polytecnica de Catalunya (2005)
Starczewski, J.T.: A triangular type-2 fuzzy logic system. In: IEEE International Conference on Fuzzy Systems, pp. 1460–1467 (2006)
Starczewski, J.T.: On defuzzification of interval type-2 fuzzy sets. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 333–340. Springer, Heidelberg (2008)
Wang, L., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Transactions on Fuzzy Systems 22, 1414–1427 (1992)
Yager, R.R., Filev, D.P.: Fundamentals of modeling and fuzzy control (in Polish: Podstawy modelowania i sterowania rozmytego). Scientific and Technical Publishing, Warsaw (1995)
Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems 4(2) (May 1996)
Zhang, W.B., Liu, W.J.: IFCM:fuzzy clustering for rule extraction of interval type-2 fuzzy logic system. In: 46th IEEE Conference on Decision and Control, p. 5318 (2007)
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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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