An innovative hybrid neuro-wavelet method for reconstruction of missing data in astronomical photometric surveys

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7267 LNAI, Issue PART 1, 2012, Pages 21-29

  Capizzi, Giacomo ^a   Napoli, Christian ^a   Paternò, Lucio ^b  

^a UNIVERSITY OF CATANIA   (Italy)

^b INAF OSSERVATORIO ASTROFISICO DI TORINO   (Italy)

Author keywords

Kepler Mission; Missing Data; Photometry; Recurrent Neural Networks; Wavelet Theory

Indexed keywords

GRANULATION NOISE; GROUND-BASED OBSERVATIONS; KEPLER MISSION; MISSING DATA; OSCILLATION FREQUENCY; RECONSTRUCTION SYSTEMS; SIGNAL FREQUENCIES; SOLAR LIKE OSCILLATIONS; SPACE OBSERVATIONS; WAVELET THEORY;

ARTIFICIAL INTELLIGENCE; RECURRENT NEURAL NETWORKS; SOFT COMPUTING; STARS;

PHOTOMETRY;

EID: 84861075543     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-29347-4_3     Document Type: Conference Paper

References (17)

1. Chaplin, W.J., et al.: Ensemble Asteroseismology of Solar-Type Stars with the NASA Kepler Mission. Science 332(6026) (2011)
2. Koch, D.G., et al.: Kepler Mission Design, Realized Photometric Performance, and Early Science. The Astrophysical Journal Letters 713(2) (2010)
3. Gray, D.F.: The Observation and Analysis of Stellar Photospheres, 3rd edn. Cambridge Press (2005)
4. Lapedes, A., Faber, R.: A self-optimizing, nonsimmetrical neural net for content addressable memory and pattern recognition. Physica D 22, 247-259 (1986)
5. Connor, J.T., Martin, R.D., Atlas, L.E.: Recurrent Neural Networks and Robust Time Series Prediction. IEEE Trans. on Neural Networks 5(2), 240-254 (1994)
6. William, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(1), 270-280 (1989)
7. William, R.J., Zipser, D.: Experimental analysis of the real- time recurrent learning algorithm. Connection Sci. 1(1), 87-111 (1989)
8. Mandic, D.P., Chambers, J.A.: Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. Wiley, New York (2001)
9. Haykin, S.: Neural Networks and Learning Machines. Prentice-Hall, Englewood Cliffs (2008)
10. Strang, G., Nguyen, T.: Wavelets and Filter Banks. Wellesley-Cambridge Press (1996)
11. Sweldens, W.: The lifting scheme: A construction of second generation wavelets. SIAM J. Math. Anal. 26(2), 511-546 (1997)
12. Mallat, S.: A Wavelet Tour of Signal Processing, The Sparse Way, 3rd edn. Academic Press (2009)
13. Napoli, C., Bonanno, F., Capizzi, G.: An hybrid neuro-wavelet approach for longterm prediction of solar wind. In: IAU Symposium No. 274, Advances in Plasma Astrophysics, Giardini Naxos, Italy, pp. 247-249 (2010)
14. Napoli, C., Bonanno, F., Capizzi, G.: Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach. In: IAU Symposium No. 274, Advances in Plasma Astrophysics, Giardini Naxos, Italy, pp.250-252 (2010)
15. Bonanno, F., Capizzi, G., Napoli, C.: A wavelet based prediction for Wind and Solar Energy for Long-Term simulation of integrated generation systems. In: 20th IEEE International Symposium on Power Electronics, Electrical Drives, Automation and Motion, Pisa, Italy, pp. 586-592 (2010)
16. Bonanno, F., Capizzi, G., Napoli, C., et al.: An Innovative Wavelet Recurrent Neural Network Based Approach for Solar Radiation Prediction. Submitted to IEEE Transaction on Sustainable Energy
17. Gupta, M.M., Jin, L., Homma, N.: Static and Dynamic Neural Networks. Wiley, New York (2003)