A Least Squares Support Vector Machine for Condition Time Series Prediction Based on Bayesian Evidence Framework

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A Least Squares Support Vector Machine for Condition Time Series Prediction Based on Bayesian Evidence Framework

更新时间：2025-07-09

- A Least Squares Support Vector Machine for Condition Time Series Prediction Based on Bayesian Evidence Framework
- Vol. 44, Issue 10, Pages: 42-46(2010)
- 作者机构：
  
  第二炮兵工程学院自动控制工程系,西安,710025
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP277
- Online First：10 October 2010，
  
  Published：2010
- 稿件说明：
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A Least Squares Support Vector Machine for Condition Time Series Prediction Based on Bayesian Evidence Framework[J]. 2010, 44(10): 42-46.
DOI：

A Least Squares Support Vector Machine for Condition Time Series Prediction Based on Bayesian Evidence Framework[J]. 2010, 44(10): 42-46. DOI：

摘要

为实现对电子系统状态时间序列的有效预测

提出一种基于贝叶斯证据框架的最小二乘支持向量机在线预测方法.该方法以逐次增加最新状态数据并剔除最旧状态数据的方式更新最小二乘支持向量机预测模型

利用分块矩阵求逆运算简化了新旧状态数据交替增减所带来的预测模型重训问题

通过贝叶斯证据框架实现预测模型超参数的在线动态优化.应用于雷达发射机中高压电源与多注速调管的状态时间序列预测实例表明

该方法的预测精度与计算效率比自适应灰色模型方法分别高9.52%与73.26%

具有预测精度高、预测稳定性高与计算效率高的优点

适用于电子系统在线状态时间序列预测.

Abstract

A method using Bayesian evidence framework(BEF)and least squares support vector machine(LSSVM)is proposed to predict electronic system condition time series accurately. A LSSVM model for prediction is trained with all the current condition time series data. Then

the LSSVM model is iteratively updated by adopting the latest data and pruning the oldest data. Matrix transform is applied to reduce the computational cost of retraining the LSSVM model. Finally

the updated LSSVM model is dynamically optimized by BEF. Numerical experiments on radar transmitter condition time series prediction are carried out to test the effectiveness of the proposed method. The experimental results and comparisons with the conventional adaptive grey model show that the proposed method has better performance in prediction accuracy

prediction stability and computational efficiency

and that the prediction accuracy and the computational efficiency for electronic system condition time series prediction are raised by 9.52% and 73.26% respectively.

关键词

Keywords

references

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