Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks

Advances in Space Research

Volumn 40, Issue 12, 2007, Pages 1863-1870

  Wei, H L ^a   Zhu, D Q ^a   Billings, S A ^a   Balikhin, M A ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Dst index; NARX model; Nonlinear system identification; Prediction; Radial basis function (RBF)networks

Indexed keywords

ALGORITHMS; DATA STRUCTURES; DATABASE SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); LINEAR NETWORK ANALYSIS; MAGNETIC FIELDS; NONLINEAR SYSTEMS;

DST INDEX; MAGNETIC STORMS; NONLINEAR DYNAMICAL SYSTEMS; SOLAR WIND PARAMETER (VBS);

RADIAL BASIS FUNCTION NETWORKS;

EID: 36049034955     PISSN: 02731177     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asr.2007.02.080     Document Type: Article

References (45)

1. Baker D.N., Klimas A.J., McPherron R.L., and Buchner J. The evolution from weak to strong geomagnetic activity: an interpretation in terms of deterministic chaos. Geophys. Res. Lett. 17 1 (1990) 41-44
2. Billings S.A., Chen S., and Korenberg M.J. Identification of MIMO non-linear systems suing a forward regression orthogonal estimator. Int. J. Control 49 6 (1989) 2157-2189
3. Billings S.A., and Chen S. The determination of multivariable nonlinear models for dynamic systems using neural networks. In: Leondes C.T. (Ed). Neural Network Systems Techniques and Applications (1998), Academic Press, San Diego 231-278
4. Billings S.A., and Wei H.L. The wavelet-NARMAX representation: a hybrid model structure combining the polynomial models and multiresolution wavelet decompositions. Int. J. Syst. Sci. 36 3 (2005) 137-152
5. Billings S.A., and Wei H.L. A new class of wavelet networks for nonlinear system identification. IEEE Trans. Neural Netw. 16 4 (2005) 862-874
6. Billings, S.A., Wei, H.L., Balikhin, M.A. Multiscale radial basis function networks. Research Report No.894, Dept. Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK, 2005.
7. Boaghe O.M., Balikhin M.A., Billings S.A., and Alleyne H. Identification of nonlinear processes in the magnetosphere dynamics and forecasting of Dst index. J. Geophys. Res. 106 A12 (2001) 30047-30066
8. Burton R.K., McPherron R.L., and Russell C.T. An empirical relationship between interplanetary conditions and Dst. J. Geophys. Res. 80 (1975) 4204-4214
9. Chen S., Billings S.A., and Luo W. Orthogonal least squares methods and their application to non-linear system identification. Int. J. Control 50 (1989) 1873-1896
10. Chen S., Billings S.A., Cowan C.F.N., and Grant P.M. Practical identification of NARMAX models using radial basis functions. Int. J. Control 52 6 (1990) 1327-1350
11. Chen S., and Billings S.A. Neural networks for nonlinear dynamic system modelling and identification. Int. J. Control 56 2 (1992) 319-346
12. Duda R.O., Hart P.E., and Stork D.G. Pattern Recognition. Second edition (2001), John Wiley & Sons, New York
13. Efron B., and Tibshirani R.J. An Introduction to the Bootstrap (1993), Chapman & Hall, New York
14. Goertz C.K., Shan L.H., and Smith R.A. Prediction of geomagnetic activity. J. Geophys. Res. 98 A5 (1993) 7673-7684
15. Harris C.J., Hong X., and Gan Q. Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach (2002), Springer-Verlag, Berlin
16. Hartman E.J., Keeler J.D., and Kowalski J.M. Layered neural networks with Gaussian hidden units as universal approximations. Neural Comput. 2 2 (1990) 210-215
17. Hernandez J.V., Tajima T., and Horton W. Neural net forecasting for geomagnetic activity. Geophys. Res. Lett. 20 23 (1993) 2707-2710
18. Johnson J.R., and Wing S. A solar cycle dependence of nonlinearity in magnetospheric activity. J. Geophys. Res. 110 A4 (2005) Art. No. A04211
19. Klimas A.J., Vassiliadis D., and Baker D.N. Dst index prediction using data-derived analogues of the magnetospheric dynamics. J. Geophys. Res. 103 A9 (1998) 20435-20447
20. Krzanowski W.J., and Lai Y.T. A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics 44 1 (1988) 23-34
21. Leontaritis I.J., and Billings S.A. Input-output parametric models for non-linear systems. Int. J. Control 41 2 (1985) 303-344
22. Liu G.P. Nonlinear Identification and Control: A Neural Network Approach (2001), Springer, London
23. Lundstedt H., Gleisner H., and Wintoft P. Operational forecasts of the geomagnetic Dst index. Geophys. Res. Lett. 29 24 (2002) Art. No. 2181
24. McPherron R.L. Predicting the Ap index from past behaviour and solar wind velocity. Phys. Chem. Earth Part C 24 1-3 (1999) 45-56
25. O'Brien T.P., and McPherron R.L. An empirical phase space analysis of ring current dynamics: solar wind control of injection and decay. J. Geophys. Res. 105 A4 (2000) 7707-7719
26. Park J., and Sandberg I.W. Universal approximation using radial-basis-function networks. Neural Comput. 3 2 (2005) 246-257
27. Poggio T., and Girosi F.M. Networks for approximation and learning. Proc. IEEE 78 9 (1990) 1481-1497
28. Pulkkinen T.I., and Baker D.N. Global substorm cycle: what can the models tell us?. Surv. Geophys. 18 (1997) 1-37
29. Schwarz G. Estimating the dimension of a model. Ann. Stat. 6 (1978) 461-464
30. Shigeaki W., Sagawa E., Ohtaka K., and Shimazu H. Prediction of the Dst index from solar wind parameters by a neural network method. Earth Planets Space 54 (2002) 1263-1275
31. Sharifi J., Araabi B.N., and Lucas C. Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modelling. Earth Planets Space 58 3 (2006) 331-341
32. Takalo J., and Timonen J. Neural network prediction of AE data. Geophys. Res. Lett. 24 19 (1993) 2403-2406
33. Temerin M., and Li X.L. A new model for the prediction of Dst on the basis of the solar wind. J. Geophys. Res. 107 A12 (2002) Art. No. 1472
34. Temerin M., and Li X.L. Dst model for 1995-2002. J. Geophys. Res. 111 A4 (2006) Art. No. A04221
35. Vassiliadis D., Klimas A.J., Baker D.N., and Roberts D.A. A description of the solar-wind magnetosphere coupling based on nonlinear filters. J. Geophys. Res. 100 A3 (1995) 3495-3512
36. Vassiliadis D., Klimas A.J., Valdivia J.A., and Baker D.N. Models of Dst geomagnetic activity and of its coupling to solar wind parameters. Phys. Chem. Earth Part C 24 1-3 (1995) 107-112
37. Vassiliadis D., Klimas A.J., Valdivia J.A., and Baker D.N. The D-st geomagnetic response as a function of storm phase and amplitude and the solar wind electric field. J. Geophys. Res. 104 A11 (1999) 24957-24976
38. Watanabe S., Sagawa E., Ohtaka K., and Shimazu H. Prediction of the Dst index from solar wind parameters by a neural network method. Earth Planets Space 54 12 (2002) 1263-1275
39. Wei H.L., and Billings S.A. A unified wavelet-based modelling framework for nonlinear system identification: the WANARX model structure. Int. J. Control 77 4 (2004) 351-366
40. Wei H.L., Billings S.A., and Balikhin M.A. Prediction of the Dst index using multiresolution wavelet models. J. Geophys. Res. 109 (2004) 10.1029/2003JA01033 A7: Art. No. A0721
41. Wei H.L., Billings S.A., and Liu J. Term and variable selection for nonlinear system identification. Int. J. Control 77 1 (2004) 86-110
42. Wei H.L., Billings S.A., and Balikhin M.A. Wavelet based nonparametric NARX models for nonlinear input-output system identification. Int. J. Syst. Sci. 37 15 (2006) 1089-1096
43. Wing S., Johnson J.R., Jen J., Meng C.I., Sibeck D.G., Bechtold K., Freeman J., Costello K., Balikhin M., and Takahashi K. Kp forecast models. J. Geophys. Res. 110 A4 (2005) Art. No. A04203
44. Wu J.G., and Lundsted H. Neural network modelling of solar wind magnetosphere interaction. J. Geophys. Res. 102 A7 (1997) 14457-14466
45. Zhu, D., Billings, S.A., Balikhin, M., Wing, S., Alleyne, H. Multi-input data derived Dst model. J. Geophys. Res., 2007, to appear.