| 基于EMD-LSSVM的多尺度混合建模方法及其应用 |
| |
| 引用本文： | 何星,王宏力,刘永志,陆敬辉,姜伟.基于EMD-LSSVM的多尺度混合建模方法及其应用[J].红外与激光工程,2013,42(7):1737-1742. |
| |
| 作者姓名： | 何星 王宏力 刘永志 陆敬辉 姜伟 |
| |
| 作者单位： | 1.第二炮兵工程大学 自动控制系,陕西 西安 710025; |
| |
| 摘 要： | 激光陀螺漂移时间序列具有非平稳和非线性的特点，单一预测模型难以准确跟踪其变化趋势。研究了基于经验模态分解（EMD）和最小二乘支持向量机（LSSVM）的多尺度混合建模方法及在激光陀螺漂移预测中的应用。首先，利用经验模态分解将漂移时间序列分解为多个本征模式分量，在采用具有适当核函数的最小二乘支持向量机分别对这些分量进行预测后，以加权集成方式得到最终预测结果。最后，将该方法用于激光陀螺的随机漂移预测中，仿真结果表明:该方法能够准确预测激光陀螺漂移值，取得了比单一模型更好的预测效果，能够为激光陀螺的漂移补偿、故障预报和可靠性诊断提供参考。
|
| 关 键 词： | 经验模态分解 最小二乘支持向量机 贝叶斯方法 证据框架 激光陀螺漂移预测 |
| 收稿时间： | 2012-10-21 |
Multi-scale modeling method based on EMD-LSSVM and its application |
| |
| He Xing
,
Wang Hongli
,
Liu Yongzhi
,
Lu Jinghui
,
Jiang Wei.Multi-scale modeling method based on EMD-LSSVM and its application[J].Infrared and Laser Engineering,2013,42(7):1737-1742. |
| |
| Authors: | He Xing Wang Hongli Liu Yongzhi Lu Jinghui Jiang Wei |
| |
| Affiliation: | 1.Department of Automatic Control,The Second Artillery Engineering University,Xi'an 710025,China;2.The Representative Place of Tianjin,The Second Artillery,Tianjin 300308,China |
| |
| Abstract: | Due to the non-linear and non-stationary characteristics of laser gyro drift time series, it can not be predicted precisely by single forecasting model. A hybrid multi-scale modeling method based on empirical mode decomposition (EMD) and least squares support vector machines(LSSVM) was proposed, and its application in drift forecasting of laser gyro was also studied. Firstly, the drift data was decomposed into a series of intrinsic mode function via empirical mode decomposition. Secondly, Least Squares Support Vector Machines predicting models with appropriate kernel functions were constructed to predict each intrinsic mode function respectively. Thirdly, output of each predicting model were equally weighted and integrated into one output. In the end, the proposed method was used for laser gyro drift prediction. The experimental results show that the proposed prediction method which is capable of forecasting drift data precisely outperforms single Least Squares Support Vector Machines method, and can provide reference for drift compensation, fault prediction and reliability diagnoses of laser gyro. |
| |
| Keywords: | empirical mode decomposition least squares support vector machines Bayesian method evidence framework laser gyro drift prediction |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《红外与激光工程》浏览原始摘要信息 |
|
点击此处可从《红外与激光工程》下载全文 |
|
