INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.5, No.4, Mar. 2013

Identification of Parametrical Restrictions in Staic Systems in Conditions of Uncertainty

Full Text (PDF, 799KB), PP.43-54


Views:82   Downloads:0

Author(s)

Nikolay Karabutov

Index Terms

Parametrical Restriction;Static System;Domination;Algorithm;Identification;Decision-making

Abstract

The approach to an estimation of area of parametrical restrictions (APR) for static linear system on parameters in the conditions of uncertainty is of-fered. For decision-making indicators of domination of an exit of model over an exit of system and the special indicator setting admissible level of errors of domination are used. The case of the representation of area of restrictions in the form of boundaries from below and from above on a modification of parameters of system is considered. The iteration algorithm of identification of restrictions and decision-making is offered. The adaptive algorithm of an estimation of boundaries of area of parametrical restrictions is synthesized. Procedure of estimation APR on the basis of the analysis of a field of secants of system is described. Method development on a case of representation APR in the form of restriction on norm of a modification of parameters of system is given. Various forms of vectorial norms and algorithms of construction of area of parametrical restrictions corresponding to them are considered.

Cite This Paper

Nikolay Karabutov,"Identification of Parametrical Restrictions in Staic Systems in Conditions of Uncertainty", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.4, pp.43-54, 2013.DOI: 10.5815/ijisa.2013.04.04

Reference

[1]Gagliardini P., Gouriéroux C., Renault E. Efficient Derivative Pricing by Extended Method of Mo-ments. National Centre of Competence in Research Financial Valuation and Risk Management, 2005.

[2]Rossi B. Optimal tests for nested model selection with underlying parameter instability. Econometric Theory. 2005, 21 (05): 962-990.

[3]Giacomini R., Rossi B. Model comparisons in un-stable environments. ERID Working Paper 30, Duke, 2009.

[4]Magnusson L., Mavroeidis S. Identification using stability restrictions. 2012, http://econ.sciences-po.fr/sites/default/files/SCident32s.pdf.

[5] Bardsley J.M. A bound-constrained levenburg-marquardt algorithm for a parameter identification problem in electromagnetics, 2004. http://www.math.umt.edu/bardsley/papers/EMopt04.pdf.

[6]Palanthandalam-Madapusia H.J., van Peltb T. H., Bernstein D.S. Parameter consistency and quadratically constrained errors-in-variables least-squares identification, International Journal of Control, Vol. 83, No. 4, April 2010: 862–877.

[7]Van Pelt T. H., Bernstein D.S. Quadratically Con-strained Least Squares Identification, Proceedings of the American Control Conference, Arlington, VA June 25-27, 2001: 3684-3689.

[8]Corrêa M.V., Aguirre L.A., Saldanha R.R. Using steady-state prior knowledge to constrain parame-ter estimates in nonlinear system identification. IEEE transactions on circuits and systems—I: Fundamental theory and applications, 2002, 49(9): 1376-1381.

[9]Chadeev V.М., Gusev S.S. Identification with re-strictions. determining a static plant parameters es-timates, Proceedings of the VII International Con-ference “System Identification and Control Prob-lems” SICPRO 'OS Moscow January 28-31, 2008. V.A. Trapeznikov Institute of Control Sciences. Moscow: V.A. Trapeznikov Institute of Control Sciences, 2012: 261-269.

[10]Chia T.L., Chow P.C., Chizeck H.J. Recursive pa-rameter identification of constrained systems: an application to electrically stimulated muscle, IEEE Trans Biomed Eng., 1991, 38(5): 429-42.

[11]Wen-Ming Shi. Parameter Estimation with Con-straints Based on Variational Method, Journal Marine. Sci. Appl. 2010, 9: 105-108.

[12]Vanli O.A., Del Castillo E. Closed-Loop System Identification for Small Samples with Constraints. Technometrics. 2007, 49(4): 382-394.

[13]Hametner C., Jakubek S. Nonlinear identification with local model networks using GTLS tecniques and equality constraints. IEEE Trans. Neural Netw. 2011, ;22(9): 1406-18.

[14]Mead J.L., Renaut R.A. Least squares problems with inequality constraints as quadratic constraints. Linear Algebra and its Applications. 2010, 432: 1936-1949.

[15]Mazunin V.P., Dvojnikov D.A. Parametrical re-strictions in nonlinear control systems of mecha-nisms with elasticity. Electrical engineering. 2010, 5: 9-13.

[16]Karabutov N.N. Adaptive system identification: information synthesis. URSS, Moscow, 2006.

[17]Karabutov N.N. Structural identification of sys-tems: analysis of informational structures, URSS: Book house “Librokom”, Мoscow, 2009.

[18]Karabutov N.N. Structural identification of static objects: Fields, structures, methods, URSS: Book house "Librokom", Мoscow, 2011.

[19]Karabutov N.N. Structural identification of a static object by processing measurement data. Measurement Techniques, Springer US. 2009, 52(1): 7-15.

[20]Lancaster P. Theory of Matrices. Academic Press, New York, 1969.