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A hybrid algorithm for the PEM estimation of ARMAX structures

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper proposes a new methodology for the estimation of ARMAX models, based on the implementation of a hybrid optimisation algorithm and a corresponding estimation procedure. The specific algorithm attempts to interconnect the diverse characteristics of two entirely different optimisation techniques, deterministic and stochastic, combining high convergence rate with increased reliability in the search for global optimum, and it consists of a super-positioned stochastic global search, followed by an independent deterministic procedure, in which the analytical gradients of the ARMAX model are used. The corresponding estimation procedure is split into two parts, due to the mixed linear-nonlinear relationship between the prediction errors and the parameter vector, and assures the stability and invertibility of the resulted models. The parametric identification test case, which is considered in this study, refers to the estimation of an ARMAX model for the description of a half-car passive suspension system of a road vehicle.
Słowa kluczowe
Rocznik
Strony
309--320
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
  • Vehicles Laboratory, School of Mechanical Engineering, National Technical University of Athens, Iroon Politechniou 9, 15773, Zografou, Athens
  • PhD Student, Vehicles Laboratory, School of Mechanical Engineering, National Technical University of Athens, Iroon Politechniou 9, 15773, Zografou, Athens
Bibliografia
  • [1] Petsounis K.A., Fassois S.D., Parametric time domain methods for the identification of vibrating structures - A critical comparison and assessment, Mechanical Systems and Signal Processing, 15, 6, 2001, 1031 -1060.
  • [2] Ljung L., System Identification: Theory for the user, Prentice-Hall PRT, 2nd Ed., New Jersey, 1999.
  • [3] Dennis J.E., Schnabel R.B., Numerical Methods for Unconstrained Optimization and Nonlinear Equations, SIAM, Philadelphia, 1996.
  • [4] Nocedal J., Yuan Y., Combining trust region and line search techniques, Advances in Nonlinear Programming, 153-175, 1998.
  • [5] Kristinsson K., Dumont G.A., System Identification and Control using Genetic Algorithms, IEEE Transactions on Systems, Man, and Cybernetics, 22, 1033-1046.
  • [6] Tan K.C., Li Y., Grey-box model identification via evolutionary computing, Control Engineering Practice, 10, 7, 673-684, 2002.
  • [7] Gray G.J., Murray-Smith D.J., Li Y., Sharman K.C., Weinbrenner Т., Nonlinear model structure identification using genetic programming, Control Engineering Practice, 6, 1341-1352, 1998.
  • [8] Billings S.A., Mao K.Z., Structure detection for nonlinear rational models using genetic algorithms, International Journal of Systems Science, 29, 3, 223-231, 1998.
  • [9] Rodriguez-Vazquez K., Fonseca C.M., Fleming P.J., Multiobjective genetic programming: A nonlinear system identification application, Late breaking papers at the 1997 Genetic Programming Conference, 207-212, 1997.
  • [10] Kanarachos A., Koulocheris D., Dertimanis V., Vrazopoulos H., Identification of a robotic arm using optimization methods for model estimation. Proceedings of IBEC 2002, Paris, France, 2002.
  • [11] Fleming P.J., Purshouse R.C., Evolutionary algorithms in control systems engineering: a survey, Control Engineering Practice, 10, 11, 1223-1241, 2002.
  • [12] Schoenauer М., Sebag М., Using Domain knowledge in Evolutionary System Identification., Evolutionary Methods for Design, Optimization and Control, Proceedings of Eurogen 2001, 35-42, 2002.
  • [13] Dertimanis V., Koulocheris D., Vrazopoulos H., Kanarachos A. , Time-series parametric modelling using Evolution Strategy with deterministic mutation operators, IFAC International Conference on Intelligent Control Systems and Signal Processing, Faro, Portugal, 328-333, 2003.
  • [14] Koulocheris D., Dertimanis V., Vrazopoulos H., Evolutionary parametric identification of dynamic systems, Forschung im Ingenieurwesen, 68, 4, 173-181, 2004.
  • [15] Koh C.G., Chen Y.F., Liaw C.-Y., A hybrid computational strategy for identification of structural parameters, Computers & Structures, 81, 107-117.
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  • [17] Schwefel H.P., Evolution & Optimum Seeking, John Wiley & Sons Inc., New York, 1995.
  • [18] Baeck Т., Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York, 1996.
  • [19] Michalewicz Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Zurich, 1996.
  • [20] Baeck Т., Schwefel H.P., An Overview of Evolutionary Algorithms for Parameter Optimization, Evolutionary Computation, 1, 1, 1-23, 1993.
  • [21] Dorigo M., Bonabeau E., Theraulaz G., Ant algorithms and stigmergy, Future Generation Computer Systems, 16, 851-871, 2000.
  • [22] Colorni A., Dorigo M., Maffioli F., Maniezzo V., Righini G., Trubian M., Heuristics from nature for hard combinatorial optimization problems, International Transactions on Operational Research, 3, 1, 1-21, 1996.
  • [23] Monmarche N., Venturini G., Slimane M.. On how Pachycondyla apicalis ants suggest a new search algorithm, Future Generation Computer Systems, 16, 937-946, 2000.
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  • [27] Schnabel R.B., Eskow E., A Revised Modified Cholesky Factorization Algorithm, SIAM Journal on Optimization, 9, 4, 1135-1148, 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP1-0053-0098
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