<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This work presents short- and medium-term predictions of length of day (LOD) up to 500 days by means of extreme learning machine (ELM). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. The influences of the solid Earth and ocean tides and seasonal atmospheric variations are removed from the C04 series. The residuals are used for training of the ELM. The results of the prediction are compared with those from other prediction methods. The accuracy of the prediction is equal to or even better than that by other approaches. The most striking advantages of employing ELM instead of other algorithms are its noticeably reduced complexity and high computational efficiency.</p></abstract>

This work presents short- and medium-term predictions of length of day (LOD) up to 500 days by means of extreme learning machine (ELM). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. The influences of the solid Earth and ocean tides and seasonal atmospheric variations are removed from the C04 series. The residuals are used for training of the ELM. The results of the prediction are compared with those from other prediction methods. The accuracy of the prediction is equal to or even better than that by other approaches. The most striking advantages of employing ELM instead of other algorithms are its noticeably reduced complexity and high computational efficiency.

Extreme Learning Machine for the Predictions of Length of Day

National Time Service Center, Chinese Academy of Sciences, Xi'an 710600, China

Key Laboratory of Primary Time and Frequency Standards, Chinese Academy of Sciences, Xi'an 710600, China

University of Chinese Academy of Sciences, Beijing 100049, China

Artificial Satellites

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Extreme Learning Machine for the Predictions of Length of Day"}

This work presents short- and medium-term predictions of length of day (LOD) up to 500 days by means of extreme learning machine (ELM). The EOP C04 time-series...

Extreme Learning Machine for the Predictions of Length of Day

Data publikacji: 01 mar 2015

Zakres stron: 19 - 33

Otrzymano: 14 lis 2014

Przyjęty: 27 lut 2015

DOI: https://doi.org/10.1515/arsa-2015-0002

Słowa kluczowe
Earth orientation parameters (EOP), length of day (LOD), predictions, artificial neural networks (ANN), extreme learning machine (ELM)

© 2015 Artificial Satellites

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Extreme Learning Machine for the Predictions of Length of Day

Data publikacji: 01 mar 2015

Zakres stron: 19 - 33

Otrzymano: 14 lis 2014

Przyjęty: 27 lut 2015

DOI: https://doi.org/10.1515/arsa-2015-0002

Słowa kluczoweEarth orientation parameters (EOP), length of day (LOD), predictions, artificial neural networks (ANN), extreme learning machine (ELM)

© 2015 Artificial Satellites

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Earth orientation parameters (EOP), length of day (LOD), predictions, artificial neural networks (ANN), extreme learning machine (ELM)