Prediction for Remote Sensing Satellite Vibration Based on Time Sequence Regression and SVM Algorithm

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (17) : 137-141.

FU Zhong-guang1，CAO Hong-fang1，QI Min-fang1

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Abstract

A support vector machine (SVM) model based on time series principle can be constructed by support vector machine algorithm and time series. From the analysis about a large number of remote sensing satellites vibration data, we can find the vibration law is the harmonic vibration with some random fluctuations. Training set and prediction set of the model can be dynamically updated by using Principle of time series, and then build the online prediction model .The model test results show that this method can predict accurate vibration trends in advance, offering help to the rapid vibration suppression measures.

Key words

remote sensing satellite Vibration / SVM;time series / ARMA model / online prediction

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FU Zhong-guang1，CAO Hong-fang1，QI Min-fang1. Prediction for Remote Sensing Satellite Vibration Based on Time Sequence Regression and SVM Algorithm[J]. Journal of Vibration and Shock, 2015, 34(17): 137-141

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