Nonlinear dynamical-information magnetosphere models for space weather forecasting
Heading:
1Cheremnykh, OK, 2Sidorenko, VI, 1Yatsenko, VA 1Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine, Kyiv, Ukraine 2National Academy of Sciences of Ukraine, Kyiv, Ukraine |
Kosm. nauka tehnol. 2008, 14 ;(1):77-84 |
https://doi.org/10.15407/knit2008.01.077 |
Publication Language: Russian |
Abstract: We describe dynamical-information magnetosphere models for Dst index forecasting in the form of nonlinear "black-box". We assume the presence of a weak turbulence for the magnetosphere plasma that depends on the solar wind speed and south magnetic field component. We propose nonlinear dynamical-information magnetosphere models to forecast the Dst index using decomposition procedure of nonlinear perturbation in the series of correlation functions. The models allow us to forecast the evolution of the Dst index for 100 hours ahead in the absence of anomalous solar wind perturbations.
|
Keywords: Dst index, magnetosphere, nonlinear perturbation |
References:
1. Akasofu S.-I., Chapman S. Solar-Terrestrial Physics, Vol. 1, 384 p. (Mir, Moscow, 1975) (Vols. 1-2; Vol. 1) [in Russian].
2. Gladkov L. A., Kureichik V. V., Kureichik V. M. Genetic Algorithms, 320 p. (Fizmatlit, Moscow, 2006) [in Russian].
3. Kuntsevich V. M. Control under Uncertainty: Guaranteed Results in Control and Identification Problems, 261 p. (Naukova Dumka, Kyiv, 2006) [in Russian].
4. Ljung L. System Identification: Theory for the User, Transl. from Eng., Ed. by Ya. Z. Tsipkin, 442 p. (Nauka, Moscow, 1991) [in Russian].
5. Tsytovich V. N. Theory of turbulent plasma, 423 p. (Atomizdat, Moscow, 1970) [in Russian].
6. Balikhin M., Bales I., Walker S. N. Identification of linear and nonlinear processes in space plasma turbulence. Adv. Space Res., 28, 787—800 (2001).
https://doi.org/10.1016/S0273-1177(01)00515-4
https://doi.org/10.1016/S0273-1177(01)00515-4
7. Billings S. A., Chen S. Extended model set, global data and threshold model identification of severely nonlinear systems. Int. J. Control., 50, 1897—1923 (1980).
https://doi.org/10.1080/00207178908953473
https://doi.org/10.1080/00207178908953473
8. Billings S. A., Voon W. S. F. Structure detection and model validity tests in the identification of nonlinear systems. IEEE Proc., 130 (4), 193—199 (1983).
https://doi.org/10.1049/ip-d.1983.0034
https://doi.org/10.1049/ip-d.1983.0034
9. Billings S. A., Zhu Q. M. Nonlinear model validation using correlation tests. Int. J. Control., 60, 1107—1120 (1994).
https://doi.org/10.1080/00207179408921513
https://doi.org/10.1080/00207179408921513
10. Boyd S., Chua L. O. Fading memory and the problem of approximating nonlinear operators with Volterra series. IEEE Trans. Circuit Syst., 32 (11), 1150— 1161 (1985).
https://doi.org/10.1109/TCS.1985.1085649
https://doi.org/10.1109/TCS.1985.1085649
11. Chankong V., Haimes Y. Y. Multi-objective decision making: theory and methodology, 442 p. (New York, 1983).
12. Chen S., Billings S. A. Representations of non-linear systems: the NARMAX model. Int. J. Control., 49 (3), 1013—1032 (1989).
https://doi.org/10.1080/00207178908559683
https://doi.org/10.1080/00207178908559683
13. Galeev A., Krasnosel'skikh V. V., Lobzin V. Fine structure of the front of a quasi-perpendicular supercritical collision-less shock wave. Sov. J. Plasma Phys., 14, 697 (1988).
14. Gleisner H., Lundstedt H., and Wintoft P. Predicting geomagnetic storms from solar wind data using time-delay neural networks. Ann. Geophysicae, 14, 679—686 (1996).
https://doi.org/10.1007/s00585-996-0679-1
https://doi.org/10.1007/s00585-996-0679-1
15. Johansen T. A. Constrained and regularized system identification. IFAC Symp. on System Identification, P. 5 (Fukuoda, Japan, 1997).
https://doi.org/10.1016/S1474-6670(17)43039-4
https://doi.org/10.1016/S1474-6670(17)43039-4
16. Johansen T. A. Multi-objective identification of FIR models. IFAC Symp. on System Identification, P. 6 (Santa Barbara, USA, 2000).
https://doi.org/10.1016/S1474-6670(17)39870-1
https://doi.org/10.1016/S1474-6670(17)39870-1
17. Leontaritis I. J., Billings S. A. Input-output parametric models for nonlinear systems. Part I. Deterministic nonlinear systems. Int. J. Control., 41, 309— 328 (1985).
18. Leontaritis I. J., Billings S. A. Model selection and validation methods for nonlinear systems. Int. J. Control, 311—341 (1987).
https://doi.org/10.1080/00207178708933730
https://doi.org/10.1080/00207178708933730
19. Ljung L. Non-linear black box models in system identification. In: Proc. IFAC Symp. on Advanced Control of Chemical Processes, 1—13 (Banff, Canada, 1997).
https://doi.org/10.1016/S1474-6670(17)43131-4
https://doi.org/10.1016/S1474-6670(17)43131-4
20. McCaffrey D., Bates I., Balikhin M., et al. Experimental method for identification of three wave coupling in space Plasma. Adv. Space Res., 25 (7-8), 1571—1577 (2000).
https://doi.org/10.1016/S0273-1177(99)00670-5
https://doi.org/10.1016/S0273-1177(99)00670-5
21. Pardalos P., Yatsenko V. Optimization and control of Lyapunov exponents. J. Optimiz. Theory and Appl., No. 1, 21—37 (2006).
22. Sagdeev R. Z., Galeev A. A. Nonlinear plasma theory. (Benjamin, White Plains, New York, 1969).
23. Tulleken H. J. A. F. Grey-box modelling and identification using physical knowledge and bayesian techniques. Automatica, 29 (2), 285—308 (1993).
https://doi.org/10.1016/0005-1098(93)90124-C
https://doi.org/10.1016/0005-1098(93)90124-C
24. Zakharov V. E., Musher S. L., Rubenchik A. M. Hamiltonian approach to the description of non-linear plasma phenomena. Phys. Reports, 129 (5), 285—366 (1985).
https://doi.org/10.1016/0370-1573(85)90040-7
https://doi.org/10.1016/0370-1573(85)90040-7