Space Weather Forecasting

IEEE Control Systems

Volumn 27, Issue 5, 2007, Pages 109-123

  Palanthandalam Madapusi, Harish J ^a   Bernstein, Dennis S ^a   Ridley, Aaron J ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; MAGNETIC FIELD MEASUREMENT; MAGNETIC FIELDS; MAGNETOMETERS; OPTIMIZATION; SOLAR WIND;

GROUND-BASED MAGNETOMETER; MAGNETIC FIELD FLUCTUATIONS; MODIFIED SUBSPACE ALGORITHM; SPACE WEATHER FORECASTING;

WEATHER FORECASTING;

EID: 35148889350     PISSN: 1066033X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCS.2007.904649     Document Type: Article

References (41)

1. D.H. Hathaway, The Solar Wind, Solar Physics [Online]. Available: http://science.msfc.nasa.gov/ssl/pad/solar/sun_wind.htm.
2. S.F. Odenwald, The 23rd Cycle. New York: Columbia Univ. Press, 2001.
3. I. Sandahl, “Does space weather really matter on the ground?” in Proc. COSPAR Space Weather Study Using Multipoint Techniques, vol. 12, Sept. 2000, pp. 349–357.
4. S.-I. Akasofu, “The development of the auroral substorm,” Planet. Space Sci., vol. 101, pp. 273-282,1964.
5. K.G. Powell, P.L. Roe, T.J. Linde, T.I. Gombosi, and D.L. De Zeeuw, “A solution-adaptive upwind scheme for ideal magnetohydrodynamics,” J. Comput. Phys., vol. 154, pp. 284-309,1999.
6. J.G. Lyon, J.A. Fedder, and C.M. Mobarry, “The Lyon-Fedder-Mobarry (LFM) global MHD magnetospheric simulation code,” J. Atmos. Solar-Terr. Phys., vol. 66, pp. 1333-1350,2004.
7. R.S. Weigel, A.J. Klimas, and D. Vassiliadis, “Solar wind coupling to and predictability of ground magnetic fields and their time derivatives,” J. Geophys. Res., vol. 108, no. A7, pp. 16-1-16-14,2003.
8. D.N. Baker, R.S. Weigel, E.J. Rigler, R.L. McPherron, D. Vassiliadis, C.N. Arge, G.L. Siscoe, and H.E. Spence, “Sun-to-magnetosphere modeling: CISM forecast model development using linked empirical methods,” J. Atmos. Solar-Terr. Phys., vol. 66, pp. 1491-1497,2004.
9. D. Baker, “Statistical analyses in the study of solar wind-magnetosphere coupling,” in Solar Wind-Magnetosphere Coupling, Y. Kamide and J. Slavin, Eds. Norwell, MA: Kluwer, 1986, pp. 17–38.
10. D. Bauer and B. Ninness, “Asymptotic Properties of least-squares estimates of hammerstein-wiener models,” Int. J. Contr., vol. 75, no. 1, pp. 34-51,2002.
11. I.W. Hunter and M.J. Korenberg, “The identification of nonlinear biological systems: Wiener and Hammerstein cascade models,” Biol. Cybern., vol. 55, pp. 135-144,1986.
12. Z. Hasiewicz, “Identification of a linear system observed through zero-memory non-linearity,” Int. J. Syst. Sci., vol. 18, no. 9, pp. 1595-1607,1987.
13. C.H. Chen and S.D. Fassois, “Maximum likelihood identification of stochastic wiener-hammerstein-type non-linear systems,” Mech. Syst. Signal Processing, vol. 6, no. 2, pp. 135-153,1992.
14. E.-W. Bai, “An optimal two-stage identification algorithm for Hammer-stein-Wiener nonlinear systems,” Automatica, vol. 34, no. 3, pp. 333-338,1998.
15. C.-H. Chen and S.D. Fassois, “On the estimation of stochastic Wiener-Hammerstein-type systems with non-smooth non-linearity,” in Proc. American Contr. Conf., Albuquerque, NM, 1997, pp. 1494–1498.
16. W. Greblicki, “Nonparametric approach to Wiener system identification,” IEEE Trans. Circuits Syst., vol. 44, no. 6, pp. 538-545,1997.
17. M. Lovera, T. Gustafsson, and M. Verhaegen, “Recursive subspace identification of linear and non-linear Wiener state-space models,” Automatica, vol. 36, pp. 1639–1650, 2000.
18. G.A. Pajunen, “Recursive identification of Wiener type nonlinear systems,” in Proc. American Contr. Conf., Boston, MA, June 1985, pp. 1365–1370.
19. T. Wigren, “Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model,” IEEE Trans. Automat. Contr., vol. 39, no. 11, pp. 2191-2206,1994.
20. D.H. Baldelli, R. Lind, and M. Brenner, “Nonlinear aeroelastic/aeroser-voelastic modeling by block-oriented identification,” AIAA J. Guid. Contr. Dyn., vol. 28, pp. 1056-1064,2005.
21. V. Verdult. Nonlinear System Identification: A State-Space Approach. Enshede, NL: Twente Univ. Press, 2002. Available: http://doc.utwente.nl/38684/1/toooool8.pdf/
22. V. Verdult and M. Verhaegen, “Kernel Methods for subspace identification of multivariable LPV and bilinear systems,” Automatica, vol. 41, no. 9, pp. 1557–1565, 2005.
23. V. Verdult and M. Verhaegen, “Identification of multivariable linear parameter-varying systems based on subspace techniques,” in Proc. Conf. Dec. Contr., Sydney, Australia, Dec. 2000, pp. 1567–1572.
24. V. Verdult, M. Verhaegen, C.T. Chou, and M. Lovera, “Efficient and systematic identification of MIMO bilinear state space models,” in Proc. Conf. Dec. Contr., Tampa, FL, Dec. 1998, pp. 1260–1265.
25. J.J. Hench, “A technique for the identification of linear periodic state-space models,” Int. J. Contr., vol. 62, no. 2, pp. 289-301,1995.
26. M. Verhaegen and X. Yu, “A class of subspace model identification algorithms to identify periodically and arbitrarily time-varying systems,” Automatica, vol. 31, no. 2, 201-216,1995.
27. S.L. Lacy and D.S. Bernstein, “Subspace identification of nonlinear systems with measured-input nonlinearities,” Int. J. Contr., vol. 78, no. 12, pp. 906–926, Aug. 2005.
28. H. Palanthandalam-Madapusi, J.B. Hoagg, and D.S. Bernstein, “Basis-function optimization for subspace-based nonlinear identification of systems with measured-input nonlinearities,” in Proc. American Contr. Conf., Boston, MA, July 2004, pp. 4788–4793.
29. H. Palanthandalam-Madapusi, A.J. Ridley, and D.S. Bernstein, “Identification and prediction of ionospheric dynamics using a Hammerstein-Wiener model,” in Proc. American Contr. Conf., Portland, OR, June 2005, pp. 5052–5057.
30. I. Goethals, K. Pelckmans, J.A.K. Suykens, and B. De Moor, “Subspace identification of hammerstein systems using least squares support vector machines,” IEEE Trans. Automat. Contr., vol. 50, no. 10, pp. 1509–1519, 2005.
31. P. Van Overschee and B. De Moor, “N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems,” Automatica, vol. 30, no. 1, pp. 75–93, Jan. 1994.
32. P. Van Overschee and B. De Moor, Subspace Identification for Linear Systems: Theory Implementation, Applications. Norwell, MA: Kluwer, 1996.
33. S.-I. Akasofu, “Predicting geomagnetic storms as a space weather project,” in Proc. COSPAR Space Weather Study Using Multipoint Techniques, vol. 12, Sept. 2000, pp. 3–20.
34. O. Nelles. Nonlinear System Identification. New York: Springer-Verlag, 2001.
35. I. Goethals, K. Pelckmans, J.A.K. Suykens, and B. De Moor, “Identification of MIMO Hammerstein model using least squares support vector machines,” Automatica, vol. 41, no. 7, pp. 1263–1272, July 2005.
36. F. Six. What is the magnetosphere? MSFC Space Plasma Physics, NASA. [Online]. Available: http://science.nasa.gov/ssl/pad/sppb/edu/magnetosphere/bullets.html
37. E.R. Christian and A.J. Davis. “Advanced Composition Explorer.” [Online]. Available: http://www.srl.caltech.edu/ACE/
38. A.J. Ridley, “Estimation of the uncertainty in timing the relationship between magnetospheric and solar wind processes,” J. Atmos. Solar-Terr. Phys., vol. 62, pp. 757–771, 2000.
39. A.N. Tikhonov, “Solution of incorrectly formulated problems and the regularization method,” Sov. Math. Doklady, vol. 4, pp. 1035–1038, 1963.
40. H.J. Palanthandalam-Madapusi, A.J. Ridley, and D.S. Bernstein. Realtime Web-based Prediction of Magnetometer Data. [Online]. Available: http://amie.engin.umich.edu/~hpalanth
41. USDOC/NOAA/NESDIS/National Geophysical Data Center. Available: http://www.ngdc.noaa.gov/