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Fuzzy dynamic programming: interpolative reasoning for an efficient derivation of optimal control policies

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We consider multistage optimal control of a fuzzy dynamic system under fuzzy constraints on controls and fuzzy goals on states in the setting of Bellman and Zadeh (1970) and Kacprzyk (1983, 1997). First, we present the solution by dynamic programming chich is a standard techniques in the class of problems considered. We indicato its limitations, mainly related to its inherent curse of dimensionality. We propose to replace the source problem by its auxiliary counterpart with a small number of reference fuzzy states and reference fuzzy controls, solve it by dynamic programming to obtain optimal reference control policies relating optimal reference fuzzy controls to reference fuzzy states. Then, we show the use of an interpolative reasoning approach to derive optimal fuzzy controls, not necessarily reference ones, for current fuzzy states, not necessarily reference ones.
Rocznik
Strony
63--84
Opis fizyczny
Bibliogr. 37 poz.
Twórcy
autor
  • Systems Research Institute, Polish Academy of Sciences ul. Newelska 6, 01-447 Warsaw, Poland and PIAP – Industrial Research Institute of Automation and Measurements Warsaw, Poland
Bibliografia
  • 1. Baldwin J.F. and Pilsworth B.W. (1982) Dynamic programming for fuzzy systems with fuzzy environment. Journal of Mathematical Analysis and Applications 85: 1–23.
  • 2. Baranyi P., K´oczy L.T., and Gedeon, T.D. (2004) A generalized concept for fuzzy rule interpolation. IEEE Transactions on Fuzzy Systems 12(6): 820–837.
  • 3. Bellman R.E. and Zadeh L.A. (1970) Decision making in a fuzzy environment. Management Science 17: 141–164.
  • 4. Chang Y.-C., Chen S.-M. and Liau C.-J. (2008) Fuzzy interpolative reasoning for sparse fuzzy-rule-based systems based on the areas of fuzzy sets. IEEE Transactions on Fuzzy Systems 16 (5): 1285-1301.
  • 5. Chen S.-M. and Ko Y.-K. (2008) Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on α-cuts and transformations techniques. IEEE Transactions on Fuzzy Systems 16 (6): 1626-1648.
  • 6. Cross V.V. and Sudkamp T.A. (2002) Similarity and Compatibility in Fuzzy Set Theory. Springer, Heidelberg and New York.
  • 7. Dubois D. and Prade H.(1992) Gradual rules in approximate reasoning. Information Sciences 61: 103–122.
  • 8. Francelin R.A.F., Kacprzyk J. and Gomide F.A.C. (2001a) Neural Network based algorithm for dynamic system optimization. Asian Journal of Control 3 (2): 131–142.
  • 9. Francelin R.A.F., Kacprzyk J. and Gomide F.A.C. (2001b) A biologically inspired neural network for dynamic programming. International Journal of Neural Systems 11 (6): 561-572.
  • 10. HsiaoW.H., Chen S.M. and Lee C.H. (1998) A new interpolative reasoning method in sparse rule-based systems. Fuzzy Sets and Systems 93 (1): 17–22.
  • 11. Kacprzyk J. (1978a) A branch-and-bound algorithm for the multistage control of a nonfuzzy system in a fuzzy environment. Control and Cybernetics 7: 51–64.
  • 12. Kacprzyk J. (1978b) Decision-making in a fuzzy environment with fuzzy termination time. Fuzzy Sets and Systems 1: 169–179.
  • 13. Kacprzyk J. (1979) A branch-and-bound algorithm for the multistage control of a fuzzy system in a fuzzy environment. Kybernetes 8: 139–147.
  • 14. Kacprzyk J. (1983) Multistage Decision Making under Fuzziness. Verlag T¨ UV Rheinland, Cologne.
  • 15. Kacprzyk J. (1993a) Interpolative reasoning in optimal fuzzy control. Proceedings of Second IEEE International Conference on Fuzzy Systems: FUZZ–IEEE’93 (San Francisco, CA, USA), II. IEEE Press, 1259–1263.
  • 16. Kacprzyk J. (1993b) Fuzzy control with an explicit performance function using dynamic programming and interpolative reasoning. Proceedings of First European Congress on Fuzzy and Intelligent Technologies – EU-FIT‘93 (Aachen, Germany), 3, Verlag der Augustinus Buchhandlung, Aachen, 1459–1463.
  • 17. Kacprzyk J. (1993c) Interpolative reasoning for computationally efficient optimal fuzzy control. Proceedings of Fifth International Fuzzy Systems Association World Congress ’93 (Seoul, Korea), Vol. II, 1270–1273.
  • 18. Kacprzyk J. (1995a) A genetic algorithm for the multistage control of a fuzzy system in a fuzzy environment. Proceedings of Joint Third International IEEE Conference on Fuzzy Systems and Second International Symposium on Fuzzy Engineering – FUZZ-IEEE’95/IFES’95 (Yokohama, Japan), III. IEEE Press, 1083–1088
  • 19. Kacprzyk J. (1995b)Multistage fuzzy control using a genetic algorithm. Proceedings of Sixth World International Fuzzy Systems Association Congress (Saõ Paulo, Brazil), II, 225–228
  • 20. Kacprzyk J. (1995c) A modified genetic algorithm for multistage control of a fuzzy system. In: H. Zimmermann, ed.,Proceedings of Third European Congress on Intelligent Techniques and Soft Computing – EUFIT’95 (Aachen, Germany), 1. Verlag Mainz, 463–466.
  • 21. Kacprzyk J. (1995d) Interpolative reasoning for computationally efficient optima multistage fuzzy control. In: Z. Bien and K.C. Min, eds., Fuzzy Logic and its Applications in Engineering, Information Sciences and Intelligent Systems. Kluwer, Dordrecht, 215–224.
  • 22. Kacprzyk J (1996) Multistage control under fuzziness using genetic algorithms. Control and Cybernetics 25: 1181–1215.
  • 23. Kacprzyk J. (1997) Multistage Fuzzy Control. Wiley, Chichester.
  • 24. Kacprzyk J. and Fedrizzi M. (1995) Developing a fuzzy logic controller in case of sparse testimonies. International Journal of Approximate Reasoning 12 (3/4): 221–236.
  • 25. Kacprzyk J., Romero R.A.M. and Gomide F.A.C. (1999) Involving objective and subjective aspects in multistage decision making and control under fuzziness: dynamic programming and neural networks. International Journal of Intelligent Systems 14 (1): 79-104.
  • 26. Kacprzyk J. and Staniewski P.(1982) Long-term inventory policy-making through fuzzy decision-making models. Fuzzy Sets and Systems 8: 117–132.
  • 27. Kacprzyk J. and Staniewski P. (1983) Control of a deterministic system in a fuzzy environment over an infinite planning horizon. Fuzzy Sets and Systems 10: 291–298.
  • 28. Kóczy L.T. and Hirota K. (1993a) Interpolative reasoning with insufficient evidence in sparse fuzzy rule base. Information Sciences 71: 169–201.
  • 29. Kóczy L.T. and Hirota K.(1993b) Approximate reasoning by linear rule interpolation and general approximation. International Journal of Approximate Reasoning 9: 197–225.
  • 30. Kóczy L.T. and Hirota K.(1997) Size reduction by interpolation in fuzzy rule bases. IEEE Transaction on Systems, Man and Cybernetics SMC-27: 14–25.
  • 31. Kóczy L.T., Hirota K. and Gedeon T.D. (2000) Fuzzy rule interpolation by the conservation of relative fuzziness. International Journal of Advanced Computational Intelligence 4 (1): 95–101.
  • 32. Tikk D. and Baranyi P. (2000) Comprehensive analysis of a new fuzzy rule interpolation method. IEEE Transactions on Fuzzy Systems 8 (3): 281–296.
  • 33. Yam Y., Wong M.L. and Baranyi P. (2006) Interpolation with function space representation of membership functions. IEEE Transactions on Fuzzy Systems 14 (3): 398–411.
  • 34. Yan S., Mizumoto M., and Qiao W.Z. (1995) Reasoning conditions on Koczy’s interpolative reasoning method in sparse fuzzy rule bases. Fuzzy Sets and Systems 75 (1): 63–71.
  • 35. Wong K.W., Tikk D., Gedeon T.D. and Kóczy L.T. (2005) Fuzzy rule interpolation for multidimensional input spaces with applications: a case study. IEEE Transactions on Fuzzy Systems 13 (6): 809–819.
  • 36. Wu Z.Q., Mizumoto M. and Shi Y. (1996) An improvement to Koczy and Hirotas interpolative reasoning in sparse fuzzy rule bases. International Journal of Approximate Reasoning 15 (3): 185-201.
  • 37. Zadeh L.A. and J. Kacprzyk, eds. (1992) Fuzzy Logic for the Management of Uncertainty. Wiley, New York.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a037729d-018a-4d45-9023-a2281a7e7ab7
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