Abstract
This paper presents an approach to formally model Takagi–Sugeno–Kang (TSK) fuzzy systems without the use of any external components. In order to keep the model continuity, the formal simulation model for a TSK fuzzy system is comprised of three types of reusable sub-models involving primitive operations. Thus, the model can be executed even on limited computational platforms, such as embedded controllers.
This work was supported by the IT R&D Program of MKE/KEIT [10035708, “The Development of CPS (Cyber-Physical Systems) Core Technologies for High Confidential Autonomic Control Software”].
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References
Hu X (2004) A simulation-based software development methodology for distributed real-time systems. Doctoral dissertation, The University of Arizona
Moallemi M, Gutierrez-Alcaraz JM, Wainer G (2008) ECD++ A DEVS based real-time simulator for embedded systems. In: Proceedings of the spring simulation multiconference, article no. 12
Park J, Yoo J (2010) Hardware-aware rate monotonic scheduling algorithm for embedded multimedia systems. ETRI J 32:657–664
Garcia AM, Baumgartner B, Schreiber U, Krane M, Knoll A, Bauernschmitt R (2009) Automedic: fuzzy control development platform for a mobile heart-lung machine. IFMBE Proc 25:685–688
Muruganandam M, Madheswaran M (2009) Modeling and simulation of modified fuzzy logic controller for various types of DC motor drives. In: Proceedings of international conference on control, automation, communication and energy conservation, pp 1–6
Lee HY, Park SM, Cho TH (2010) Simulation modeling of SAM fuzzy logic controllers. IEICE Trans Inf Syst E93-D:1984–1986
Jamshidi M, Sheikh-Bahaei S, Kitzinger J, Sridhar P, Beatty S, Xia S, Wang Y, Song T, Dole U, Lie J (2003) V-LAB–A distributed intelligent discrete-event environment for autonomous agents simulation. Intell Autom Soft Comput 9:181–214
Lee HY, Kim HJ (2009) Reducing the complexity of DEVS-based mamdani models for enhancing privacy. Proceedings of international symposium on advanced intelligent systems, pp 281–283
Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. IEEE Proc 121:1585–1588
Zeigler BP, Kim TG, Praehofer H (2000) Theory of modeling and simulation, 2nd edn. Academic Press, New York
Kosko B (1997) Fuzzy engineering. Prentice Hall, Upper Saddle River
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 15:116–132
Sugeno M, Kang KT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28:15–33
Yen J, Langari R (1999) Fuzzy logic: intelligence control and information. Prentice Hall, Englewood Cliffs
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Lee, H.Y., Kim, J.M., Chun, I., Kim, WT., Park, SM. (2011). Simulation Modeling of TSK Fuzzy Systems for Model Continuity. In: Park, J., Arabnia, H., Chang, HB., Shon, T. (eds) IT Convergence and Services. Lecture Notes in Electrical Engineering, vol 107. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2598-0_8
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DOI: https://doi.org/10.1007/978-94-007-2598-0_8
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