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Membership-set estimation with genetic algorithms in nonlinear models

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this article, a procedure for characterizing the feasible parameter set of nonlinear models with a membership-set uncertainty description is provided. A specific Genetic Algorithm denominated e-GA has been developed, based on Evolutionary Algorithm for Multiobjective Optimization, to find the global minima of the multimodal functions appearing when the robust identification problem is formulated. These global minima define the contour of the feasible parameter set. The procedure makes it possible to obtain the feasible parameter non-convex even disjoint set. It is not necessary for the model to be differentiable with respect to the unknown parameters. An example is presented which determines the feasible parameter set of a nonlinear model of a thermal process. In this case, noise affects the output process (interior temperature) and besides model errors appear.
Czasopismo
Rocznik
Strony
67--76
Opis fizyczny
Bibliogr. 12 poz., rys., wykr.
Twórcy
  • Department of Systems Engineering and Control, Polytechnic University of Valencia, Camino de Vera 14, P.O. Box 22012 E-46071 Valencia, Spain
autor
  • Department of Systems Engineering and Control, Polytechnic University of Valencia, Camino de Vera 14, P.O. Box 22012 E-46071 Valencia, Spain
  • Department of Systems Engineering and Control, Polytechnic University of Valencia, Camino de Vera 14, P.O. Box 22012 E-46071 Valencia, Spain
autor
  • Department of Systems Engineering and Control, Polytechnic University of Valencia, Camino de Vera 14, P.O. Box 22012 E-46071 Valencia, Spain
Bibliografia
  • [1] Chisci L., Garulli A., Vicing A., Zappa G., Block recursive parallelotopic bounding in set membership identification, Automatica, Vol. 34, No. 1, 1998, 15-22.
  • [2] Coello C., Veldhuizen D., Lamont G., Evolutionary algorithms for solving multi-objective problems, Kluwer Academic Publishers, New York 2002.
  • [3] Deb K., Mohan M., Mishra S., A fast multi-objective evolutionary algorithm for finding well-spread paretooptimal solution. Technical Report 2003002, KanGAL 2003.
  • [4] Fogel E., Huang F., On the value of information in system identification-bounded noise case, Automatica, Vol. 18, No. 12, 1982, 229-238.
  • [5] Garulli A., Reinelt W., Model error modelling in set membership identification, Proc. of the System Identification Symposium, 2000.
  • [6] Goodwin G., Braslavsky J., Senon M., Non-stationary stochastic embedding for transfer function estimation, Proc. of the 14 th IFAC World Congress, Bejing, China 1999.
  • [7] Laumanns M., Thiele L., Deb K., Zitzler E., Combining convergence and diversity in evolutionary multi-objective optimization. Evolutionary Computation, Vol. 10, No. 5, 2002.
  • [8] Norton J., Veres S., Identification of nonlinear state-space models by deterministic search, Proc. of the IFAC Symposium on Identification and System Parameter Estimation, Vol. 1, 199!, 363-368.
  • [9] Reinelt W., Garulli A., Ljung L., Comparing different approaches to model error modelling in robust identification, Automatica, Vol. 38, No. 5, 2002, 787-803.
  • [10] Sanchez-PeÑa R., Sznaier M., Robust systems theory and applications, John Wiley & Sons, 1998.
  • [11] Walter E., Piet-Lahanier H., Recursive robust minmax estimation for models linear in their parameters, Proc. of the IFAC Symposium on Identification and System Parameter Estimation, Vol. I, 1991,763-768.
  • [12] Walter, E., Kieffer M., Interval analysis for guaranteed nonlinear parameter estimation, Proc. of the 13th IFAC Symposium on System Identification, 1991.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0009-0017
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