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基于WD-LSSVM的监护信息预报研究

Study on Prediction of Monitoring Information Based on WD-LSSVM
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摘要 针对监护信息预报问题,提出了一种新的监护信息预报方法。采用小波分解和最小二乘支持向量机相结合的方法,该方法利用小波分解将原参数序列分解为逼近信号和细节信号,采用LS-SVM算法,分别建立监护信息逼近信号和细节信号的预报模型,得到逼近信号和细节信号的监护信息预报值,然后将各部分预报值利用小波逆变换进而得到监护信息最终预报结果。最后对提出的方法进行了实验验证。 According to the forecast problem of monitoring information, the method of wavelet decomposition and LS-SVM is put forward. Physiological parameter is decomposed into approximation signal and the detail signal by means of the method, and the forecast results are obtained with prediction models of the approximation signal and the detail signal which are established respectively using LS-SVM, and then the final prediction results of monitoring information using inverse wavelet transform with the forecast results before. Finally the proposed method is verified by experiment.
作者 蒋贤海 谢存禧
机构地区 广东水利电力职业技术学院机械工程系 华南理工大学机械与汽车工程学院
出处 《机电产品开发与创新》 2013年第2期112-114,共3页 Development & Innovation of Machinery & Electrical Products
基金 粤港关键领域重点突破项目(20090101-1)
关键词 监护信息 小波分解 最小二乘支持向量机 预报 monitoring information wavelet decomposition LS-SVM forecast
分类号 TP23 [自动化与计算机技术—检测技术与自动化装置]
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参考文献9

  • 1Vapnik V.N. An overview of statistical learning theory [J]. IEEE Trans Neural Network, 1999,5.
  • 2Christensen, R. and Pearson, L.M. Prediction diagnostics for spatial linear models. Biometrika.1992,79.
  • 3Zhang Zhengdao,Hu Shousong.Fault prediction of fighter based on nonparametric density estimation[J].Journal of Systems Engineering and Electronics,2005,16(4):831-836. 被引量：3
  • 4Xu K, Xie M, etc. Application of Neural Networks in forecasting Engine Systems Reliability[J], Applied Soft Computing. 2003,2.
  • 5HANBAY D.An expert system based on least square support vector machines for diagnosis of the valvular heart disease [J].Expert Systems with Applications, 2008.
  • 6Box and Jenkins;顾岚,范金城(译).时间序列分析预测与控制[M].中国统计出版社.2003.
  • 7Policker S, Geva A. B. A New Algorithm for Time Series Prediction by Temporal Fuzzy Clustering[A]. Proceedings of 15th International Conference on Pattern Recognition[C], ICPR.2000.
  • 8邵信光,杨慧中,陈刚.基于粒子群优化算法的支持向量机参数选择及其应用[J].控制理论与应用,2006,23(5):740-743. 被引量：127
  • 9Moody G B, Mark R G, Goldberger A L. PhysioNet: a web-based resource for the study of physiologic signals[J]. IEEE Engineering in Medicine and Biology Society, 2001,3.

二级参考文献12

  • 1邵信光,杨慧中,石晨曦.ε不敏感支持向量回归在化工数据建模中的应用[J].东南大学学报（自然科学版）,2004,34(B11):215-218. 被引量：6
  • 2VAPNIK V N.The Nature of Statistical Learning Theory[M].New York:Springer-Veriag,1995.
  • 3SMOLA A J,SCHOLKOPF B.A tutorial on support vector regression[R].NeuroCOLT2 Technical Report NC2-TR-1998-030.London:Royal Holloway College,University of London,1998.
  • 4SANCHEZ A D.Advanced support vector machines and kernel methods[J].Neurocomputing,2003,55(1):5-20.
  • 5CHAPPELLE O,VAPNIK V,BOUSQUET O,et al.Choosing multiple parameters for support vector machines[J].Machine Learning,2002,46(1):131-160.
  • 6CHEN Pengwei,WANG Jungying,LEE Hahnming.Model selection of SVMs using GA approach[C]//Proc of 2004 IEEE Int Joint Conf on NeuralNetworks.Piscataway,NJ:IEEE Press,2004:2035-2040.
  • 7ZHENG Chunhong,JIAO Licheng.Automatic parameters selection for SVM based on GA[C]//Proc of the 5th World Congress on Intelligent Control and Automation.Piscataway,NJ:IEEE Press,2004:1869 -1872.
  • 8KENNEDY J,EBERHART R.Particle swarm optimization[C] //Procof IEEE Int Conf on Neural Networks,Perth,Australia.Piscataway,NJ:IEEE Press,1995:1942-1948.
  • 9EBERHART R,KENNEY J.A new optimizer using particle swarm theory[C]//Proc of the Sixth International Symposium on Micro Machine and Human Science,Nagoya,Japan.Piscataway,NJ:IEEE Press,1995:39-43.
  • 10VANDEN B F,ENGELBRECHT A P.Training product uhit networks using cooperative particle swarm optimizers[C]]//Proc of the Third Genetic and Evolutionary Computation Conference (GECCO),San Francisco,USA.Piscataway,NJ:IEEE Press,2001:126-132.

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机电产品开发与创新
2013年 第2期

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