适用于小子样时间序列预测的动态回归极端学习机

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摘要针对设备状态在线监测中的小子样建模问题，提出一种基于动态回归极端学习机（dynamic regressionextreme leaming machine，DR-ELM）的设备状态在线监测方法．该方法利用设备状态数据训练基于DR-ELM的预测模型，通过逐次增加新数据与删减旧数据的方式，对DR-ELM预测模型进行在线训练，从而实现对设备状态的准确预测．混沌时间序列预测仿真与基于时间序列预测的风机状态监测实例表明，相比于极端学习机（extreme learningmachine，ELM）与在线贯序极端学习机（on-line sequential extreme learning machine，OS-ELM），该方法的计算效率与预测精度更高．

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	张弦
	王宏力

关键词 ：极端学习机, 在线训练, 小子样, 时间序列预测, 状态监测

Abstract：To deal with the problem of small-sample modeling in equipment condition on-line monitoring,an on-line monitoring method based on dynamic regression extreme learning machine(DR-ELM) is proposed.Condition data of mechanical equipment are used to train a prediction model based on DR-ELM.In an iterative manner,the latest condition data are adopted and the oldest condition data are abandoned,to achieve the DR-ELM prediction model training on-line.Thus, the current condition of mechanical equipment can be effectively predicted by the method.Simulation on chaotic time series prediction and fan condition monitoring based on time series prediction indicate that the method has better performance in training computational cost and prediction accuracy in comparison with conventional condition monitoring methods based on extreme learning machine(ELM) and on-line sequential extreme learning machine(OS-ELM).

Key words： extreme learning machine(ELM) on-line training small-sample time series prediction condition monitoring

收稿日期: 2010-07-13 出版日期: 2011-10-20

TP277

通讯作者: 张弦 E-mail: sltecas@163.com

作者简介: 张弦（1982- ），男，博士生．研究领域为工业过程的故障监控．
王宏力（1965- ），男，博士，教授．研究领域为控制系统的故障检测与诊断．

引用本文:

张弦, 王宏力. 适用于小子样时间序列预测的动态回归极端学习机[J]. 信息与控制, 2011, 40(5): 704-709.
ZHANG Xian, WANG Hongli. Dynamic Regression Extreme Learning Machine and Its Application to Small-sample Time Series Prediction. Information and control, 2011, 40(5): 704-709.

链接本文:

http://ic.sia.cn/CN/ 或 http://ic.sia.cn/CN/Y2011/V40/I5/704

[11]	Huang G B,Chen L.Enhanced random search based incremental extreme learning machine[J].Neurocomputing,2008,71(16/17/18):3460-3468.
[15]	Liu N,Wang H.Ensemble based extreme learning machine[J].IEEE Signal Processing Letters,2010,17(8):754-757.
[4]	Zhao F G,Chen J,Guo L,et al.Neuro-fuzzy based condition prediction of bearing health[J].Journal of Vibration and Control,2009,15(7):1079-1091.
[17]	Lan Y,Soh C Y,Huang G B.Two-stage extreme learning machine for regression[J].Neurocomputing,2010,73(16/17/18):3028-3038.
[2]	Tran V T,Yang B S,Tan A C C.Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems[J].Expert Systems with Applications,2009,36(5):9378-9387.
[6]	Liu Z,Zou S Y,Liu S Q,et al.Condition prediction of hydroelectric generating unit based on immune optimized RBFNN[M]//Lecture Notes in Computer Science:vol.5263.Berlin,Germany:Springer,2008:864-872.
[19]	张贤达.矩阵分析与应用[M].北京:清华大学出版社,2005.Zhang X D.Matrix analysis and applications[M].Beijing:Tsinghua University Press,2005.
[3]	Feng F Z,Zhu D D,Jiang P C,et al.GA-EMD-SVR condition prediction for a certain diesel engine[C]//Proceedings of 2010 Prognostics and System Health Management Conference.Piscataway,NJ,USA:IEEE,2010:1-8.
[7]	陈果.用结构自适应神经网络预测航空发动机性能趋势[J].航空学报,2007,28(3):535-539.Chen G.Forecasting engine performance trend by using structure self-adaptive neural network[J].Acta Aeronautica et Astronautica Sinica,2007,28(3):535-539.
[13]	Feng G,Huang G B,Lin Q P,et al.Error minimized extreme learning machine with growth of hidden nodes and incremental learning[J].IEEE Transactions on Neural Networks,2009,20(8):1352-1357.
[14]	Miche Y,Soriamaa A,Bas P,et al.OP-ELM:Optimally pruned extreme learning machine[J].IEEE Transactions on Neural Networks,2010,21(1):158-162.
[20]	李德刚.设备预知维护的体系理论及支撑技术研究[D].长沙:湖南大学,2006.Li D G.Research on the system theory and support technology of plant predictive maintenance[D].Changsha:Hunan University,2006.
[8]	Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:Theory and applications[J].Neurocomputing,2006,70(1/2/3):489-501.
[9]	Huang G B,Liang N Y,Rong H J,et al.On-line sequential extreme learning machine[C]//Proceedings of the IASTED International Conference on Computational Intelligence.Anaheim,CA,USA:Acta Press,2005:4-6.
[10]	Liang N Y,Huang G B,Saratchandran N,et al.A fast and accurate on-line sequential learning algorithm for feedforward networks[J] IEEE Transactions on Neural Networks,2006,17(6):1411-1423.
[12]	Chen L,Zhou L F,Pung H K.No-reference image quality assessment using modified extreme learning machine classifier[J].Applied Soft Computing,2009,9(2):541-552.
[1]	Caesarendra W,Niu G,Yang B S.Machine condition prognosis based on sequential Monte Carlo method[J].Expert Systems with Applications,2010,37(3):2412-2420.
[5]	Yan G H,Zhu Y S.Application research of local support vector machines in condition trend prediction of reactor coolant pump[M]//Advances in Soft Computing:vol.116.Berlin,Germany:Springer,2009:35-43.
[18]	Malathi V,Marimuthu N S,Baskar S.Intelligent approaches using support vector machine and extreme learning machine for transmission line protection[J].Neurocomputing,2010,73(10/11/12):2160-2167.
[16]	Lan Y,Soh C Y,Huang G B.Constructive hidden nodes selection of extreme learning machine for regression[J].Neurocomputing,2010,73(16/17/18):3191-3199.