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Method of Detection Abnormal Features in Ionosphere Critical Frequency Data on the Basis of Wavelet Transformation and Neural Networks Combination
Institute of Cosmophysical Researches and Radio Wave Propagation FEB RAS, Petropavlovsk-Kamchatskij, Russia; Kamchatka State Technical University, Petropavlovsk-Kamchatskij, Russia.
Institute of Cosmophysical Researches and Radio Wave Propagation FEB RAS, Petropavlovsk-Kamchatskij, Russia; Kamchatka State Technical University, Petropavlovsk-Kamchatskij, Russia
Institute of Cosmophysical Researches and Radio Wave Propagation FEB RAS, Petropavlovsk-Kamchatskij, Russia
Kamchatka State Technical University, Petropavlovsk-Kamchatskij, Russia
- 1 Institute of Cosmophysical Researches and Radio Wave Propagation FEB RAS, Petropavlovsk-Kamchatskij, Russia; Kamchatka State Technical University, Petropavlovsk-Kamchatskij, Russia.
- 2 Institute of Cosmophysical Researches and Radio Wave Propagation FEB RAS, Petropavlovsk-Kamchatskij, Russia; Kamchatka State Technical University, Petropavlovsk-Kamchatskij, Russia
- 3 Institute of Cosmophysical Researches and Radio Wave Propagation FEB RAS, Petropavlovsk-Kamchatskij, Russia
- 4 Kamchatka State Technical University, Petropavlovsk-Kamchatskij, Russia
Journal of Software Engineering and Applications·Volume 05 (2013)·Pages 181–187·Published 17 January 2013·DOI10.4236/jsea.2012.512B035
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Abstract
The research is focused on the development of automatic detection method of abnormal features, that occur in recorded time series of ionosphere critical frequency fOF2 during periods of high solar or seismic activity. The method is based on joint application of wavelet-transformation and neural networks. On the basis of wavelet transformation algorithms for the detection of features and estimation of their parameters were developed. Detection and analysis of characteristic components of time series are performed on the basis of joint application of wavelet transformation and neural networks. Method's approbation is performed on fOF2 data obtained at the observatory “Paratunka” (Paratunka settlement, Kamchatskiy Kray).
Keywordswavelet transformationneural networkscritical frequency of ionosphereabnormalitiesearthquakes
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