柔性支持向量回归及其在故障检测中的应用

易辉; 宋晓峰; 姜斌; 刘宇芳; 周智华

doi:10.3724/SP.J.1004.2013.00272

柔性支持向量回归及其在故障检测中的应用

doi: 10.3724/SP.J.1004.2013.00272

1.
南京航空航天大学自动化学院南京 210016;
2.
国电科学技术研究院南京 210031

详细信息

通讯作者:
姜斌

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出版历程
- 收稿日期: 2011-09-15
- 修回日期: 2012-10-14
- 刊出日期: 2013-03-20

Flexible Support Vector Regression and Its Application to Fault Detection

1.
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016;
2.
Guodian Science and Technology Research Institute, Nanjing 210031

摘要

摘要: 支持向量回归(Support vector regression, SVR)的学习性能及泛化性能取决于参数设置.在常规方法中,这些参数以固定值形式参与运算,而当面对复杂分布的数据集时, 可能无法挑选出一组能够胜任各种分布情况的参数,参数设置需要在过拟合和欠拟合之间进行取舍. 因此,本文提出一种能够根据样本分布进行参数自我调整的柔性支持向量回归算法(Flexible support vector regression, F-SVR).该算法根据样本分布的复杂度,将训练样本划分为多个区域,在训练过程中, F-SVR为不同区域设置不同的训练参数,有效避免了过拟合与欠拟合.本文首先采用一组人工数据对所提算法有效性进行验证,在实验中, F-SVR在保持学习能力的同时,具备较传统方法更优秀的泛化性能.最后,本文将该算法运用至高频电源故障的实际检测,效果良好.
- 支持向量回归 /
- 柔性 /
- 故障检测 /
- 电源
Abstract: Hyper-parameters, which determine the ability of learning and generalization for support vector regression (SVR), are usually fixed during training. Thus when SVR is applied to complex system modeling, this parameters-fixed strategy leaves the SVR in a dilemma of selecting rigorous or slack parameters due to complicated distributions of sample dataset. Therefore, in this paper we proposed a flexible support vector regression (F-SVR) in which parameters are adaptive to sample dataset distributions during training. The method F-SVR divides the training sample dataset into several domains according to the distribution complexity, and generates a different parameter set for each domain. The efficacy of the proposed method is validated on an artificial dataset, where F-SVR yields better generalization ability than conventional SVR methods while maintaining good learning ability. Finally, we also apply F-SVR successfully to practical fault detection of a high frequency power supply.
- Support vector regression (SVR) /
- flexible /
- fault detection /
- power supply

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