Natural Data Analysis Method Based on Wavelet Filtering and NARX Neural Networks
A method for analyzing natural data and detecting anomalies is proposed. The method is based on combining wavelet filtering operations with the NARX neural network. The analysis of natural data and the detection of anomalies are of particular relevance in the problems of geophysical monitoring. An i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/33/1/63 |
| _version_ | 1797381178761674752 |
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| author | Oksana Mandrikova Yurii Polozov Bogdana Mandrikova |
| author_facet | Oksana Mandrikova Yurii Polozov Bogdana Mandrikova |
| author_sort | Oksana Mandrikova |
| collection | DOAJ |
| description | A method for analyzing natural data and detecting anomalies is proposed. The method is based on combining wavelet filtering operations with the NARX neural network. The analysis of natural data and the detection of anomalies are of particular relevance in the problems of geophysical monitoring. An important requirement of these methods is their adaptability, accuracy and efficiency. Efficiency makes it possible to detect anomalies timely in order to prevent catastrophic natural phenomena. Wavelet filtering operations include the application of a multi-scale analysis construction and threshold functions. The article proposes a wavelet filtering algorithm and a method for estimating thresholds based on a stochastic approach. The operations of the method implementation are described. It is shown that the use of wavelet filtering allows one to suppress noise, simplifies the data structure and, as a result, allows one to obtain a more accurate NARX neural network model. The effectiveness of the method for detecting ionospheric anomalies during periods of magnetic storms is shown using the data of the critical frequency of the ionosphere as an example. |
| first_indexed | 2024-03-08T20:47:38Z |
| format | Article |
| id | doaj.art-3f001a4e0c4d4d409139b8731e40b88f |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-08T20:47:38Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-3f001a4e0c4d4d409139b8731e40b88f2023-12-22T14:06:57ZengMDPI AGEngineering Proceedings2673-45912023-08-013316310.3390/engproc2023033063Natural Data Analysis Method Based on Wavelet Filtering and NARX Neural NetworksOksana Mandrikova0Yurii Polozov1Bogdana Mandrikova2Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS, 684034 Paratunka, Kamchatskiy Krai, RussiaInstitute of Cosmophysical Research and Radio Wave Propagation FEB RAS, 684034 Paratunka, Kamchatskiy Krai, RussiaInstitute of Cosmophysical Research and Radio Wave Propagation FEB RAS, 684034 Paratunka, Kamchatskiy Krai, RussiaA method for analyzing natural data and detecting anomalies is proposed. The method is based on combining wavelet filtering operations with the NARX neural network. The analysis of natural data and the detection of anomalies are of particular relevance in the problems of geophysical monitoring. An important requirement of these methods is their adaptability, accuracy and efficiency. Efficiency makes it possible to detect anomalies timely in order to prevent catastrophic natural phenomena. Wavelet filtering operations include the application of a multi-scale analysis construction and threshold functions. The article proposes a wavelet filtering algorithm and a method for estimating thresholds based on a stochastic approach. The operations of the method implementation are described. It is shown that the use of wavelet filtering allows one to suppress noise, simplifies the data structure and, as a result, allows one to obtain a more accurate NARX neural network model. The effectiveness of the method for detecting ionospheric anomalies during periods of magnetic storms is shown using the data of the critical frequency of the ionosphere as an example.https://www.mdpi.com/2673-4591/33/1/63data analysiswaveletsneural networksionosphere |
| spellingShingle | Oksana Mandrikova Yurii Polozov Bogdana Mandrikova Natural Data Analysis Method Based on Wavelet Filtering and NARX Neural Networks Engineering Proceedings data analysis wavelets neural networks ionosphere |
| title | Natural Data Analysis Method Based on Wavelet Filtering and NARX Neural Networks |
| title_full | Natural Data Analysis Method Based on Wavelet Filtering and NARX Neural Networks |
| title_fullStr | Natural Data Analysis Method Based on Wavelet Filtering and NARX Neural Networks |
| title_full_unstemmed | Natural Data Analysis Method Based on Wavelet Filtering and NARX Neural Networks |
| title_short | Natural Data Analysis Method Based on Wavelet Filtering and NARX Neural Networks |
| title_sort | natural data analysis method based on wavelet filtering and narx neural networks |
| topic | data analysis wavelets neural networks ionosphere |
| url | https://www.mdpi.com/2673-4591/33/1/63 |
| work_keys_str_mv | AT oksanamandrikova naturaldataanalysismethodbasedonwaveletfilteringandnarxneuralnetworks AT yuriipolozov naturaldataanalysismethodbasedonwaveletfilteringandnarxneuralnetworks AT bogdanamandrikova naturaldataanalysismethodbasedonwaveletfilteringandnarxneuralnetworks |