| 引用本文: | 张军峰,胡寿松.基于聚类和支持向量机的非线性时间序列故障预报[J].控制理论与应用,2007,24(1):64~68.[点击复制] |
| ZHANG Jun-feng, HU Shou-song.Nonlinear time series fault predictionbased on clustering and support vector machines[J].Control Theory and Technology,2007,24(1):64~68.[点击复制] |
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| 基于聚类和支持向量机的非线性时间序列故障预报 |
| Nonlinear time series fault predictionbased on clustering and support vector machines |
| 摘要点击 1779 全文点击 2095 投稿时间：2005-08-01 修订日期：2006-02-23 |
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| DOI编号 10.7641/j.issn.1000-8152.2007.1.011 |
| 2007,24(1):64-68 |
| 中文关键词 故障预报 K-均值聚类 支持向量回归 时间序列预测 |
| 英文关键词 fault prediction K-means
clustering support vector regression time series prediction |
| 基金项目 国家自然科学基金重点资助项目 60234010;航空科学基金资助项目 05E52031; 国防基础科研资助项目K1603060318 |
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| 中文摘要 |
| 针对非线性时间序列故障预报问题,
提出了一种基于聚类和支持向量机的方法.
将正常的时间序列按照K-均值聚类算法进行聚类学习,
同时利用支持向量机回归的时间序列预测算法获得 预测序列,
然后通过比较聚类所得的正常原型和预测序列的相似性实现故障预报.
仿真结果表明: 本文提出的
方法更能满足实时性的要求, 也更为准确. |
| 英文摘要 |
| Based on clustering
and support vector machines, a new method is proposed to
solve the nonlinear time series fault prediction. The normal time series is clustered using K-means clustering
algorithm to get the normal prototype. Meanwhile, the predicting series is obtained by time series predicting
algorithm based on support vector regression. Fault prediction can also be implemented by calculating the
similarity between the normal prototype and the predicting series. Finally, the simulation results indicate
that the proposed method can predict the fault more quickly and more accurately. |