基于相空间重构的自适应残差修正支持向量回归预测算法

doi:10.11772/j.issn.1001-9081.2016.11.3229

计算机应用 ›› 2016, Vol. 36 ›› Issue (11): 3229-3233.DOI: 10.11772/j.issn.1001-9081.2016.11.3229

基于相空间重构的自适应残差修正支持向量回归预测算法

李俊山^1,2, 仝奇³, 叶霞², 许元¹

1. 东莞理工学院城市学院, 广东东莞 523419;
2. 火箭军工程大学信息工程系, 西安 710025;
3. 中国人民解放军96261部队, 河南灵宝 471700

收稿日期:2016-04-19 修回日期:2016-06-30 出版日期:2016-11-10 发布日期:2016-11-12
通讯作者: 李俊山
作者简介:李俊山(1956-),男,陕西白水人,教授,博士,CCF杰出会员,主要研究方向:故障诊断与维修保障、图像处理、计算机视觉;仝奇(1988-),男,河南开封人,助理工程师,硕士,主要研究方向:电子设备故障诊断;叶霞(1977-),女,江苏南京人,副教授,博士,CCF会员,主要研究方向:指挥信息系统、网络安全;许元(1986-),男,陕西武功人,助教,硕士,主要研究方向:智能检测。
基金资助:
装备维修科学研究与改革项目。

Adaptive residual error correction support vector regression prediction algorithm based on phase space reconstruction

LI Junshan^1,2, TONG Qi³, YE Xia², XU Yuan¹

1. City College, Dongguan University of Technology, Dongguan Guangdong 523419, China;
2. Department of Information Engineering, Rocket Force University of Engineering, Xi'an Shaanxi 710025, China;
3. 96261 Unit of PLA, Lingbao Henan 471700, China

Received:2016-04-19 Revised:2016-06-30 Online:2016-11-10 Published:2016-11-12
Supported by:
This work is partially supported by the Scientific Research and Reform project of Equipment Maintenance.

摘要/Abstract

摘要： 针对模拟电路故障预测存在的非线性时间序列预测问题和传统支持向量回归（SVR）多步预测时出现的误差累积问题，提出了一种基于相空间重构的自适应残差修正SVR预测算法。首先，分析了SVR多步预测方法对时间序列趋势预测的意义和多步预测导致的误差积累问题；其次，将相空间重构技术引入SVR预测中，对表征模拟电路状态的时间序列进行相空间重构，并进而进行SVR预测；然后，在对多步预测过程中产生的误差累积序列进行二次SVR预测的基础上，实现对初始预测误差的自适应修正；最后，对所提算法进行了预测仿真验证。仿真验证和模拟电路的健康度预测实验结果表明，所提算法能有效降低多步预测导致的误差积累，显著提高回归估计精度，更好地预测模拟电路状态的变化趋势。

关键词: 支持向量回归, 多步预测, 误差累积, 相空间重构, 残差

Abstract: Focusing on the problem of nonlinear time series prediction in the field of analog circuit fault prediction and the problem of error accumulation in traditional Support Vector Regression (SVR) multi-step prediction, a new adaptive SVR prediction algorithm based on phase space reconstruction was proposed. Firstly, the significance of SVR multi-step prediction method for time series trend prediction and the error accumulation problem caused by multi-step prediction were analyzed. Secondly, phase space reconstruction technique was introduced into SVR prediction, the phase space of the time series of the analog circuit state was reconstructed, and then the SVR prediction was carried out. Thirdly, on the basis of the two SVR prediction of the error accumulated sequence generated in the multi-step prediction process, the adaptive correction of the initial prediction error was realized. Finally, the proposed algorithm was simulated and verified. The simulation verification results and experimental results of the health degree prediction of the analog circuit show that the proposed algorithm can effectively reduce the error accumulation caused by multi-step prediction, and significantly improve the accuracy of regression estimation, and better predict the change trend of analog circuit state.

Key words: Support Vector Regression (SVR), multi-step prediction, error accumulation, phase space reconstruction, residual

中图分类号:

TP391

李俊山, 仝奇, 叶霞, 许元. 基于相空间重构的自适应残差修正支持向量回归预测算法[J]. 计算机应用, 2016, 36(11): 3229-3233.

LI Junshan, TONG Qi, YE Xia, XU Yuan. Adaptive residual error correction support vector regression prediction algorithm based on phase space reconstruction[J]. Journal of Computer Applications, 2016, 36(11): 3229-3233.

参考文献

[1] 张朝龙, 何怡刚, 邓芳明, 等. 一种基于QPSO-RVM的模拟电路故障预测方法[J]. 仪器仪表学报, 2014, 35(8):1751-1757.(ZHANG C L, HE Y G, DENG F M, et al. An approach for analog circuit fault prognostics based on QPSO-RVM[J]. Chinese Journal of Scientific Instrument, 2014, 35(8):1751-1757.)
[2] 罗慧. 模拟电路测试诊断理论与关键技术研究[D]. 南京:南京航空航天大学, 2012. (LUO H. Research on testing diagnosis theory and key technology for analog circuits[D]. Nanjing:Nanjing University of Aeronautics & Astronautics, 2012.)
[3] 张爱华, 王永超, 宋心点, 等. 鲁棒LSSVR的模拟电路性能在线评价策略[J]. 仪器仪表学报, 2013, 34(10):2328-2335. (ZHANG A H, WANG Y C, SONG X D, et al. Novel online evaluation strategy for analog circuit performance based modified robust LSSVR[J]. Chinese Journal of Scientific Instrument, 2013, 34(10):2328-2335.)
[4] 王涛, 李艾华, 高运广, 等. 基于相空间重构和遗传优化SVR的机械设备状态趋势预测[J]. 噪声与振动控制, 2014, 34(3):176-181. (WANG T, LI A H, GAO Y G, et al. Condition trend prediction of mechanical equipments based on phase space reconstruction and genetic optimization support vector regression[J]. Noise and Vibration Control, 2014, 34(3):176-181.)
[5] LAU K W, WU Q H. Local prediction of non-linear time series using support vector regression[J]. Pattern Recognition, 2008, 41(5):1539-1547.
[6] 周若愚. 基于SVR与半监督学习的时间序列预测[D]. 西安:西安电子科技大学, 2014.(ZHOU R Y. Time series prediction based on SVR and semi-supervised learning[D]. Xi'an:Xidian University, 2014.)
[7] 陈荣, 梁昌勇, 谢福伟. 基于SVR的非线性时间序列预测方法应用综述[J]. 合肥工业大学学报(自然科学版), 2013, 36(3):369-374. (CHEN R, LIANG C Y, XIE F W. Application of nonlinear time series forecasting methods based on support vector regression[J]. Journal of Hefei University of Technology (Natural Science), 2013, 36(3):369-374.)
[8] 李学斯, 毕研刚, 杨宏桥, 等. 一种支持向量回归机自动模型选择方法[J]. 计算机与现代化,2011(10):19-23. (LI X S, BI Y G, YANG H Q, et al. An automatic model selection method for support vector regression[J].Computer and Modernization, 2011(10):19-23.)
[9] 杨斌. 基于EEMD-SVR的预测模型与应用[D]. 广州:华南理工大学, 2013.(YANG B. Research of prediction and application based on EEMD-SVR model[D]. Guangzhou:South China University of Technology, 2013.)
[10] PACKARD N H, CRUTCHFIELD J P, FARMER J D, et al. Geometry from a time series[J]. Physical Review Letters, 1980, 43(9):712-715.
[11] FARASER A M, SWINNEY H L. Independent coordinates for strange attractors from mutual information[J]. Physical Review A, 1986, 33(2):1134-1140.
[12] CAO L Y, MEE A, JUDD K. Dynamics from multivariate time series[J]. Physica D, 1998, 121(1):75-88.
[13] DERRICK T R, BATES B T, DUFEK J S. Evaluation of time-series data sets using the pearson product-moment correlation coefficient[J]. Medicine and Science in Sports and Exercise, 1994, 26(7):19-28.

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基于相空间重构的自适应残差修正支持向量回归预测算法

Adaptive residual error correction support vector regression prediction algorithm based on phase space reconstruction

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