Clock Differences Prediction Algorithm Based on EMD-SVM

ZHU Jiangmiao; SUN Panpan; GAO Yuan; ZHENG Pengfei

doi:10.1049/cje.2016.08.039

Volume 27 Issue 1

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ZHU Jiangmiao, SUN Panpan, GAO Yuan, et al., “Clock Differences Prediction Algorithm Based on EMD-SVM,” Chinese Journal of Electronics, vol. 27, no. 1, pp. 128-132, 2018, doi: 10.1049/cje.2016.08.039

Citation:

ZHU Jiangmiao, SUN Panpan, GAO Yuan, et al., “Clock Differences Prediction Algorithm Based on EMD-SVM,” Chinese Journal of Electronics, vol. 27, no. 1, pp. 128-132, 2018, doi: 10.1049/cje.2016.08.039

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Clock Differences Prediction Algorithm Based on EMD-SVM

doi: 10.1049/cje.2016.08.039

1.
College of Electronic Information and Control Engineering of Beijing University of Technology, Beijing 100124, China;
2.
National Institute of Metrology, Beijing 100013, China

Funds: This work is supported by the Chinese Time (No.29-APT1501-5), and the Development and Application of Cesium Atomic Fountain Clock (No.2013YQ090943).

Received Date: 2015-12-21
Rev Recd Date: 2016-04-29
Publish Date: 2018-01-10

Abstract

Abstract

A new prediction algorithm based on Empirical model decomposition (EMD) and Support vector machine (SVM) is put forward in this paper, and this algorithm solves the problem of the hydrogen atomic clock differences prediction, which is affected by the non-linearity and non-stability. The clock differences were decomposed into Intrinsic mode functions (IMF) and the residual series. The suitable kernel function and parameters were chosen to build the different SVM for predicting each IMF and the residual series. Each prediction result was summed to obtain the clock differences prediction. Results show that the EMD-SVM algorithm is effective compared with the linear regression and single SVM. The relative prediction error is reduced from 0.4327% to 0.2371%, and the dispersion is less than other methods.
- Empirical model decomposition (EMD),
- Support vector machine (SVM),
- Clock differences prediction

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References(16)

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