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Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses

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Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present study, a combination of wavelet neural networks was adapted from previous research in order to predict daily average values of cosmic ray impulses 30 days in advance. Additional comparison of both neural network and linear regression modelling with and without wavelet decomposition was conducted for further demonstration of increased accuracy with wavelet neural networks in a simple input-output fitting model


International Letters of Chemistry, Physics and Astronomy (Volume 34)
J. M. Caswell "Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses", International Letters of Chemistry, Physics and Astronomy, Vol. 34, pp. 55-63, 2014
Online since:
May 2014

J. M. Caswell, Journal of Signal and Information Processing 5 (2014) 42-53.

L. Erguo, Y. Jinshou, Proceedings of the 4th World Congress on Intelligent Control and Automation, Shang Hai (2002) 2755-2759.

K. Levenberg, Quarterly of Applied Mathematics 2 (1944) 164-168.

B. Krishna, R. Satyaji Rao, P. C. Nayak, Journal of Water Resource and Protection 3 (2011) 50-59.

A. D. Erlykin, A. W. Wolfendale, Journal of Physics G: Nuclear and Particle Physics 23 (1997) 979.

K. S. Carslaw, R. G. Harrison, J. Kirby, Science 298 (2002) 1732-1737.

K. O'Brien, W. Friedberg, H. H. Sauer, D. F. Smart, Environmental International 22 (1996) 9-44.

F. A. Cucinotta, M. Durante, The Lancet Oncology 7 (2006) 431-435.

J. H. Adams, R. Silberberg, C. H. Tsao, IEEE Transactions on Nuclear Science 29 (1982) 169-172.

S. -H. Cao, J. -B. Chen, W. -B. Weng, J. -C. Cao, Natural Science 1 (2009) 30-36.

C. G. Looney, Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists (1997) Oxford University Press, Oxford.

K. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, Nature Medicine 7 (2001) 673-679.

R. J. Boynton, M. A. Balikhin, S. A. Billings, O. A. Amariutei, Annales Geophysicae 31 (2001) 1579-1589.

L. Adams, Microelectronics Journal 16 (1985) 17-29.

D. Binder, E. C. Smith, A. B. Holman, IEEE Transactions on Nuclear Science 22 (1975) 2675-2680.

F. Valach, J. Boschnicek, P. Hejda, M. Revallo, Advances in Space Research 53 (2014) 589-598.

K. S. Saroka, J. M. Caswell, A. Lapointe, M. A. Persinger, Neuroscience Letters 560 (2014) 126-130. ( Received 18 May 2014; accepted 26 May 2014 ).

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