Nikiforov
Vladimir O.
D.Sc., Prof.
doi: 10.17586/2226-1494-2016-16-3-565-567
IDENTIFICATION PROPERTIES ENHANCEMENT ALGORITHM FOR PROBLEMS OF PARAMETERS ESTIMATION OF LINEAR REGRESSION MODEL
Read the full article
';
For citation: Aranovskiy S.V., Bobtsov A.A., Wang J., Nikolaev N.A., Pyrkin A.A. Identification properties enhancement algorithm for problems of parameters estimation of linear regression model. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 3, pp. 565–567. doi: 10.17586/2226-1494-2016-16-3-565-567
Abstract
This brief paper describes a new approach to identification of unknown constant parameters for a linear regression model. The main idea of the method lies in transformation of initial model into a new kind one. The new model regressor possesses identification properties or meets persistency of excitation conditions. An example of two unknown parameters identification for the linear regression model shows efficiency of the proposed approach. Simulation was carried out for a regressor with no persistency of excitation conditions, hence, parameter identification is not guaranteed.
Acknowledgements. This work was partially financially supported by the Government of the Russian Federation, (Grant 074-U01) and by the Ministry of Education and Science of the Russian Federation (Project 14.Z50.31.0031).
References
1. Aranovskiy S.V., Bobtsov A.A., Pyrkin A.A. Cascade reduction approach for identification problems. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2012, no. 3(79), pp. 149–150. (In Russian)
2. Ljung L. System Identification: Theory for the User. New Jersey, Prentice-Hall, 1987, 519 p.
3. Fradkov A.L., Miroshnik I.V., Nikiforov V.O. Nonlinear and Adaptive Control of Complex Systems. Springer, 1999. 528 p. doi: 10.1007/978-94-015-9261-1
4. Andrievsky B.R., Fradkov A.L. Izbrannye Glavy Teorii Avtomaticheskogo Upravleniya s Primerami na Yazyke MATLAB [Selected Chapters of Control Theory with Examples in MATLAB]. St. Petersburg, Nauka Publ., 1999, 475 p.
5. Sastry S., Bodson M. Adaptive Control: Stability, Convergence and Robustness. Dover, 2011. 400 p.
6. Loria A., Kelly R., Teel A.R. Uniform parametric convergence in the adaptive control of mechanical systems. European Journal of Control, 2005, vol. 11, no. 2, pp. 87–100. doi: 10.3166/ejc.11.87-100
7. Derevitskii D.P., Fradkov A.L. Prikladnaya Teoriya Diskretnykh Adaptivnykh Sistem Upravleniya [Applied Theory of Digital Adaptive Control Systems]. Moscow, Nauka Publ., 1981, 216 p.
8. Fomin V.N., Fradkov A.L., Yakubovich V.A. Adaptivnoe Upravlenie Dinamicheskimi Ob"ektami [Adaptive Control of Dynamic Objects]. Moscow, Nauka Publ., 1981, 448 p.
9. Fradkov A.L. Adaptivnoe Upravlenie v Slozhnykh Sistemakh [Adaptive Control in Complex Systems]. Mos-cow, Nauka Publ., 1990, 296 p.
