A METHOD BUILD FUZZY ASSOCIATE MEMORY FOR FUZZY CONTROL PROBLEMS

Abstract

Building fuzzy associate memory accordance with control problems has great meaning for control methods  In this paper we propose a method, that build fuzzy associate memory for control problem based on optimal trajectory of control problem, the method developed by theory hedge algebras and genetic algorithm.

https://doi.org/10.29037/ajstd.258
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References

Cao, Z. and Kandel, A. (1989), Applicability of some fuzzy implication operators, Fuzzy sets and systems 31, pp. 151-186.

Davis, L. Ed., (1987), Genetic algorithms and simulated annealing, Pitman Publishers, London, England.

Goldberg, D.E. (1989), Genetic algorithm in search, optimization and machine learning, Addison-Wesley, Reading, Massachusets.

Holland, J.H. (1975), Adaptation in natural and artificial systems. The University of Michigan Press.

Ho, N.C. (2003), Quantifying Hedge Algebras and Interpolation Methods in Approximate Reasoning, Proc. of the 5th Inter. Conf. on Fuzzy Information Processing, Beijing, March 1-4, pp. 105-112.

Ho, N.C., Lan, V.N., and Viet, L.X. (2006), Quantifying Hedge Algebras. Interpolative reasoning method and its application to some problems of fuzzy control WSEAS TRANSACTIONS ON COMPUTER. Issue 11, Vol. 5, pp. 2519- 2529.

Ho, N.C., Lan, V.N., Viet, L.X. (2006), An interpolative reasoning method based on Hedge Algebras and its application to a problem of fuzzy control”, Proceedings of the 10th WSEAS International on COMPUTERS, Vouliagmeni, Athens, Greece, July 13-15, pp. 526-534.

Cat-Ho Nguyen (2007), A topological completion of refined hedge algebras and a model of fuzziness of linguistic terms and hedges. Fuzzy Sets and Systems 158(4), pp. 436-451.

Cat-Ho Nguyen and Nguyen Van Long (2007), Fuzziness measure on complete hedge algebras and quantifying semantics of terms in linear hedge algebras. Fuzzy Sets and Systems 158(4), pp. 452-471.

Koza, J.R. (1990), Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems, Report No.STAN-CS-90-1314, Stanford University.

Kiszka, J.B, Kochanska, M.E., and Sliwinska, S. The influence of some fuzzy implication operators on the accuracy of a fuzzy model-Part I, Fuzzy Sets and Systems 15(1983), 111128.

Kiszka, J.B, Kochanska, M.E., and Sliwinska, S. The influence of some fuzzy implication operators on the accuracy of a fuzzy model-Part II, Fuzzy Sets and Systems 15(1983),223240.

Ross, T.J. Fuzzy logic with Engineering application. International Edition. Mc Graw-Hill, Inc 2004 (second Ed).

Yager, R.R (1994), Aggregation operators and fuzzy systems modelling. Fuzzy Sets and Systems, 67, pp. 129-145.

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