时序回归支持向量机在卫星振动预测中的应用

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (17) : 137-141.

论文

付忠广1, 曹宏芳1, 齐敏芳1

作者信息 +

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

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

Author information +

文章历史 +

摘要

将支持向量机算法与时间序列原理相结合，可构造出基于时间序列的支持向量机模型。通过对大量遥感卫星振动数据进行分析，得出该卫星振动规律为有随机波动成分的简谐振动。应用时间序列的原理，动态更新模型训练集和预测集，构建基于时序回归的支持向量机在线预测模型。模型测试结果表明，这种方法可以比较准确有效的实现振动趋势的提前预测，为振动抑制措施的快速实现提供帮助。

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.

导出引用

付忠广1, 曹宏芳1, 齐敏芳1. 时序回归支持向量机在卫星振动预测中的应用[J]. 振动与冲击, 2015, 34(17): 137-141

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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