On-Line Support Vector Regression by Multirate Sampling with Application

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On-Line Support Vector Regression by Multirate Sampling with Application

更新时间：2025-05-01

- On-Line Support Vector Regression by Multirate Sampling with Application
- Vol. 44, Issue 3, Pages: 37-41(2010)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP271.4
- Online First：10 March 2010，
  
  Published：2010
- 稿件说明：
移动端阅览
On-Line Support Vector Regression by Multirate Sampling with Application[J]. 2010, 44(3): 37-41.
DOI：

On-Line Support Vector Regression by Multirate Sampling with Application[J]. 2010, 44(3): 37-41. DOI：

摘要

针对应用支持向量回归对不确定控制系统在线建模时精度受异常数据影响的问题

通过分析不同样本分布情况下异常数据的影响

指出增加异常数据邻域的样本密度可以有效地提高建模精度.提出了多率采样的支持向量回归在线建模方法

通过多率采样增加局部样本密度

并利用支持向量回归在小样本学习时的良好性能

构建一种局部样本密集的滚动时间窗

用以减少训练样本数和在线剔除异常数据.将该方法应用于多通道电液力伺服同步加载系统的负荷输出预测

结果表明

与传统单率采样的方法相比

在训练样本只增加2个的情况下

该方法具有更好的鲁棒性和预测精度

预测平均绝对误差达到了0.66%.

Abstract

To improve on-line modeling precision of support vector regression(SVR)in uncertain control systems

by analyzing the effects of training data sets distribution and outliers

it is demonstrated that the precision can be effectively improved by increasing data density at neighborhood of outliers. A modeling method using multirate sampling is presented based on on-line SVR. In this method

multirate sampling is chosen to increase local data density

and a local-data-intensive sliding time window is established to reduce the training data number and eliminate outliers by taking advantage of the good performance of SVR in small sample. The method is applied to a multichannel electrohydraulic force servo synchronous loading system to predict the load output. Compared with the traditional single rate sampling method

the results indicatethe better robustness and prediction accuracy. The prediction mean absolute percentage error gets 0.66% while only two training samples are added.

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references

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加权支持向量机求解路径算法研究.西安交通大学学报, 2008,42( 10):1226-1229.

ε不敏感损失函数支持向量机分类性能研究.西安交通大学学报, 2007,41(11):1315-1320.

考虑损伤程度的设备运行可靠性研究.西安交通大学学报, 2010,44(1): 36-40.

动态概率模型跟踪性能退化的实时可靠性评估方法.西安交通大学学报, 2010,44(1):46-50.

一种自适应局部线性嵌入与谱聚类融合的故障诊断方法.西安交通大学学报, 2010,44(1):77-82.

自适应滤波实时网络流量异常检测方法.西安交通大学学报, 2009,43(12):1-5.

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