References
- [1] M. Agarwal and S. Goel, Expert system and it’s requirement engineering process, in Recent Advances and Innovations in Engineering (ICRAIE), 2014, pp. 1–4, May 2014.10.1109/ICRAIE.2014.6909306
- [2] K. Zalis, Application of expert systems in diagnostics of high voltage insulating systems, in Solid Dielectrics, 2004. ICSD 2004. Proceedings of the 2004 IEEE International Conference on, vol. 2, pp. 691–694 Vol. 2, July 2004.
- [3] K. Bilal and S. Mohsin, Mu/hadith: A Cloud Based Distributed Expert System for Classification of Ahadith, in Frontiers of Information Technology (FIT), 2012 10th International Conference on, pp. 73–78, Dec 2012.10.1109/FIT.2012.22
- [4] J. T. Starczewski, General type-2 FLS with uncertainty generated by fuzzy rough sets, in Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, pp. 1–6, July 2010.10.1109/FUZZY.2010.5584238
- [5] R. K. Nowicki, B. A. Nowak, J. T. Starczewski, and K. Cpałka, The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features, in Neural Networks (IJCNN), 2014 International Joint Conference on, pp. 3759–3766, July 2014.10.1109/IJCNN.2014.6889857
- [6] J. T. Starczewski, A Triangular Type-2 Fuzzy Logic System, in Fuzzy Systems, 2006 IEEE International Conference on, pp. 1460–1467, 2006.10.1109/FUZZY.2006.1681901
- [7] N. C. Long and P. Meesad, Meta-heuristic algorithms applied to the optimization of type-1 and type 2 TSK fuzzy logic systems for sea water level prediction, in Computational Intelligence Applications (IWCIA), 2013 IEEE Sixth International Workshop on, pp. 69–74, July 2013.10.1109/IWCIA.2013.6624787
- [8] M. Kinoshita, T. Fukuzaki, T. Satoh, and M. Miyake, An automatic operation method for control rods in BWR plants, Meeting on In-Core Instrumentation and Reactor Core Assessment, Cadarache, France, 1988.
- [9] J. A. Bernard, Use of rule-based system for process control, IEEE Contr. Sys. Mag, pp. 3–13, 1988.10.1109/37.7735
- [10] F. Fujitec, FLEX-8800 series elevator group control system, Fujitec Co., Ltd., Osaka, Japan, 1988.
- [11] S. Yasunobu, S. Miyamoto, and H. Ihara, Fuzzy control for automatic train operation system, IFORS Int. Congress on Control in Transportation Systems, Baden-Baden, 1983.10.1016/B978-0-08-029365-3.50010-9
- [12] O. Itoh, K. Gotoh, T. Nakayama, and S. Takamizawa, Application of fuzzy control to activated sludge process, in Proc. 2nd IFSA Congress, Tokyo, Japan, pp. 282–285, July, 1987.
- [13] O. Yagishita, O. Itoh, and M. Sugeno, Application of fuzzy reasoning to the water purification process, in Industrial Applications of Fuuy Control, M. Sugeno, Ed. Amsterdam: North-Holland, pp. 19–40, 1985.
- [14] M. Kacprowicz and A. Niewiadomski, On Dedicated Fuzzy Logic Systems for Emission Control of Industrial Gases, in Trends in Logic XIII (A. Indrzejczak, J. Kaczmarek, and M. Zawidzki, eds.), pp. 113–130, 2014.
- [15] A. Niewiadomski and M. Kacprowicz, Higher order fuzzy logic in controlling selective catalytic reduction systems, Bulletin of the Polish Academy of Sciences Technical Sciences, vol. 62, no. 4, pp. 743–750, 2014.10.2478/bpasts-2014-0080
- [16] M. Kacprowicz and A. Niewiadomski, Managing Data on Air Pollution Using Fuzzy Controller, in Computer Methods in Practice (A. Cader, M. Yatsymirskyy, and K. Przybyszewski, eds.), pp. 46–57, Exit Publishing House, Warsaw, Poland, 2012.
- [17] K. Renkas, A. Niewiadomski, and M. Kacprowicz, Learning Rules for Hierarchical Fuzzy Logic Systems with Selective Fuzzy Controller Activation, Lecture Notes in Computer Science - Springer, vol. 9119, pp. 260–270, 2015.10.1007/978-3-319-19324-3_24
- [18] S. Prabhakar, M. Karthikeyan, K. Annamalai, and V. N. Banugopan, Control of emission characteristics by using Selective Catalytic Reduction (SCR) in D.I. diesel engine, IEEE Conference Publications, 2010.10.1109/FAME.2010.5714808
- [19] R. R. Yager and D. P. Filev, Fundamentals of modeling and fuzzy control (in Polish: Podstawy modelowania i sterowania rozmytego). WNT, Warsaw, 1995.
- [20] J. Jantzen, Foundations of Fuzzy Control. John Wiley & Sons Ltd., England, 2007.10.1002/9780470061176
- [21] D. Majumder and K. Dwijesh, Fuzzy logic and its application in technology and management. Narosa, 2007.
- [22] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall, 2001.
- [23] J. C. Fodor, Contrapositive symmetry of fuzzy implications, Fuzzy Sets and Systems, (1995).10.1016/0165-0114(94)00210-X
- [24] J. C. Fodor, On fuzzy implication, Fuzzy Sets and Systems, vol. 42, pp. 293–300, 1991.10.1016/0165-0114(91)90108-3
- [25] O. Cordon, F. Herrera, and P. Villar, Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base, Fuzzy Systems, IEEE Transactions on, vol. 9, no. 4, pp. 667–674, 2001.10.1109/91.940977
- [26] R. Hammell and T. Sudkamp, Learning Fuzzy Rules From Data, in The Application of Information Technologies (Computer Science) to Mission Systems, pp. 1–10, 1998.
- [27] K. Mittal, A. Jain, K. S. Vaisla, O. Castillo, and J. Kacprzyk, A comprehensive review on type-2 fuzzy logic applications: Past, present and future, Engineering Applications of Artificial Intelligence, vol. 95, 2020.10.1016/j.engappai.2020.103916
- [28] S. Türk, M. Deveci, E. Özcan, F. Canıtez, and R. John, Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations, Information Sciences, vol. 547, pp. 641–666, 2021.10.1016/j.ins.2020.08.076
- [29] E. Ontiveros, P. Melin, and O. Castillo, Comparative study of interval Type-2 and general Type-2 fuzzy systems in medical diagnosis, Information Sciences, vol. 525, pp. 37–53, 2020.10.1016/j.ins.2020.03.059
- [30] R. Jafelice and W. Lodwick, Interval analysis of the HIV dynamics model solution using type-2 fuzzy sets, Mathematics and Computers in Simulation, vol. 180, pp. 306–327, 2021.10.1016/j.matcom.2020.08.022
- [31] Z. Ashraf, M. L. Roy, P. K. Muhuri, and Q. Danish Lohani, Interval type-2 fuzzy logic system based similarity evaluation for image steganography, Heliyon, vol. 6, no. 5, p. e03771, 2020.10.1016/j.heliyon.2020.e03771721511632420466
- [32] E. Ontiveros-Robles and P. Melin, A hybrid design of shadowed type-2 fuzzy inference systems applied in diagnosis problems, Engineering Applications of Artificial Intelligence, vol. 86, pp. 43–55, 2019.10.1016/j.engappai.2019.08.017
- [33] J. McCulloch and C. Wagner, On the choice of similarity measures for type-2 fuzzy sets, Information Sciences, vol. 510, pp. 135–154, 2020.10.1016/j.ins.2019.09.027
- [34] Q. Liang and J. M. Mendel, Interval Type-2 Fuzzy Logic Systems: Theory and Design, IEEE Transactions on Fuzzy Systems, vol. 8, pp. 535–550, (2000).10.1109/91.873577
- [35] L. Wang and J. M. Mendel, Generating Fuzzy Rules by Learning from Examples, IEEE Transactions on Fuzzy Systems, vol. 22, pp. 1414–1427, 1992.10.1109/21.199466
- [36] D. Wu and J. M. Mendel, Recommendations on designing practical interval type-2 fuzzy systems, CoRR, vol. abs/1907.01697, 2019.10.1016/j.engappai.2019.06.012
- [37] H. H. Li and M. M. Gupta, Fuzzy Logic and Intelligent Systems. Springer, 2007.
- [38] PKN ORLEN, Annual Report 2012 PKN Orlen, tech. rep., PKN ORLEN, 2012.