基于相空间重构与鲁棒极限学习机的时延预测
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摘要
针对网络控制系统(networked control system,NCS)诱导时延具有的时变、随机、非线性等特点,提出了一种相空间重构与鲁棒极限学习机(robust extreme learning machine,RELM)的时延预测算法。首先利用0-1测试对时延序列进行混沌特性检测,再通过改进关联积分法确定重构延迟参数和嵌入维数,进而对时延序列进行重构,新的样本更能真实反映时延变化特性。以重构后的时延序列为训练样本,同时,考虑异常值的稀疏特性,运用RELM进行时延序列预测。该方法具有学习速度快、泛化性能好、可有效降低异常值影响等优点。
Aiming at the time-varying,random and nonlinear characteristics induced delay in the networked control system(NCS),a delay prediction algorithm based on phase space reconstruction and robust extreme learning machine(RELM)is proposed.Firstly,the chaotic property of delay sequence is detected by 0-1 test,and then the reconstruction delay parameters and embedding dimension are determined by an improved correlation integral method,and then the delay sequence is reconstructed.The new samples can more accurately reflect the delay variation features.Using the reconstructed delay sequence as a training sample,and considering the sparse characteristics of outliers,a robust limit learning machine is used to perform delay sequence prediction.The method has the advantages of fast learning,good generalization performance,effectively reducing the impact of outliers.
Aiming at the time-varying,random and nonlinear characteristics induced delay in the networked control system(NCS),a delay prediction algorithm based on phase space reconstruction and robust extreme learning machine(RELM)is proposed.Firstly,the chaotic property of delay sequence is detected by 0-1 test,and then the reconstruction delay parameters and embedding dimension are determined by an improved correlation integral method,and then the delay sequence is reconstructed.The new samples can more accurately reflect the delay variation features.Using the reconstructed delay sequence as a training sample,and considering the sparse characteristics of outliers,a robust limit learning machine is used to perform delay sequence prediction.The method has the advantages of fast learning,good generalization performance,effectively reducing the impact of outliers.
引文
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[2]黄玲,施菲菲,谢文博,等.网络系统的马尔可夫时滞预测控制[J].电机与控制学报,2015,19(6):89-94.HUANG L,SHI F F,XIE W B,et al.Markov delay predicttive control for network systems[J].Electric Machines and Control,2015,19(6):89-94.
[3]刘婷,姜顺,潘丰.网络控制系统前向通道随机时延的在线多步预测[J].信息与控制,2017,46(5):620-626.LIU T,JIANG S,PAN F.Online multi-step prediction for the random delay of the forward channel in networked control system[J].Information and Control,2017,46(5):620-626.
[4]宋杨,涂小敏,费敏锐.基于FARIMA模型的Internet时延预测[J].仪器仪表学报,2012,33(4):757-763.SONG Y,TU X M,FEI M R.Internet time-delay prediction based on FARIMA model[J].Chinese Journal of Scientific Instrument,2012,33(4):757-763.
[5]田中大,高宪文,李琨.基于EMD与LS-SVM的网络控制系统时延预测方法[J].电子学报,2014,42(5):868-874.TIAN Z D,GAO X W,LI K.Time-delay prediction method of networked control system based on EMD and LS-SVM[J].Chinese Journal of Electronics,2014,42(5):868-874.
[6]田中大,高宪文,李琨.基于KPCA与LSSVM的网络控制系统时延预测方法[J].系统工程与电子技术,2013,35(6):1281-1285.TIAN Z D,GAO X W,LI K.Networked control system time-delay prediction method based on KPCA and LSSVM[J].Systems Engineering and Electronics,2013,35(6):1281-1285.
[7]HUANG F,YIN K,ZHANG G,et al.Landslide groundwater level time series prediction based on phase space reconstruction and wavelet analysis-support vector machine optimized by PSO algorithm[J].Earth Science-Journal of China University of Geosciences,2015,40(7):1254-1265.
[8]WANG D,LUO H,GRUNDER O,et al.Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction[J].Renewable Energy,2017,113:1345-1358.
[9]LI S,WANG P,GOEL L.Short-term load forecasting by wavelet transform and evolutionary extreme learning machine[J].Electric Power Systems Research,2015,122:96-103.
[10]ADENAN N H,HAMID N Z A,MOHAMED Z,et al.A pilot study of river flow prediction in urban area based on phase space reconstruction[C]∥Proc.of the American Institute of Physics Conference Series,2017:040011.
[11]ZHANG P,QI B,RONG Z,et al.Optimal dissolved gas analysis data set selection based on phase space reconstruction[C]∥Proc.of the IEEE Conference on Electrical Insulation and Dielectric Phenomenon,2017:229-232.
[12]SK M I,BANERJEE A.Denoising time series by way of a flexible model for phase space reconstruction[C]∥Proc.of the Pacific-Asia Conference on Knowledge Discovery and Data Mining,2016:3-16.
[13]OUYANG Q,LU W,XIN X,et al.Monthly rainfall forecasting using EEMD-SVR based on phase-space reconstruction[J].Water resources management,2016,30(7):2311-2325.
[14]GAO Z K,SMALL M,KURTHS J.Complex network analysis of time series[J].Europhysics Letters,2016,116(5):50001.
[15]LIU H,TIAN H Q,LI Y F.Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms[J].Energy Conversion&Management,2015,100(16):16-22.
[16]DEO R C,爦AHIN M.Application of the extreme learning machine algorithm for the prediction of monthly effective drought index in eastern Australia[J].Atmospheric Research,2015,153:512-525.
[17]WANG X,HAN M.Online sequential extreme learning machine with kernels for nonstationary time series prediction[J].Neurocomputing,2014,145:90-97.
[18]SAVI M A,PEREIRA-PINTO F H I,VIOLA F M,et al.Using 0-1test to diagnose chaos on shape memory alloy dynamical systems[J].Chaos,Solitons&Fractals,2017,103:307-324.
[19]ARMAND EYEBE FOUDA J S,BODO B,SABAT S L,et al.A modified 0-1test for chaos detection in oversampled time series observations[J].International Journal of Bifurcation and Chaos,2014,24(5):1450063.
[20]BERNARDINI D,LITAK G.An overview of 0-1test for chaos[J].Journal of the Brazilian Society of Mechanical Sciences and Engineering,2016,38(5):1433-1450.
[21]XIAO Q,WANG S,ZHANG Z,et al.Analysis of sunspot time series(1749-2014)by means of 0-1test for chaos detection[C]∥Proc.of the IEEE International Conference on Computational Intelligence and Security,2016:215-218.
[22]SONG J,MENG D,WANG Y.Analysis of chaotic behavior based on phase space reconstruction methods[C]∥Proc.of the IEEE 6th International Symposium on Computational Intelligence and Design,2014:414-417.
[23]陆振波,蔡志明,姜可宇.基于改进的C-C方法的相空间重构参数选择[J].系统仿真学报,2007,19(11):2527-2529.LU Z B,CAI Z M,JIANG K Y.Determination of embedding parameters for phase space reconstruction based on improved C-C method[J].Journal of System Simulation,2007,19(11):2527-2529.
[24]TANG J,DENG C,HUANG G B.Extreme learning machine for multilayer perception[J].IEEE Trans.on Neural Networks and Learning Systems,2016,27(4):809-821.
[25]WANG X,HAN M.Improved extreme learning machine for multivariate time series online sequential prediction[J].Engineering Applications of Artificial Intelligence,2015,40:28-36.
[26]ZONG W,HUANG G B,CHEN Y.Weighted extreme learning machine for imbalance learning[J].Neurocomputing,2013,101(3):229-242.
[27]ZHANG K,LUO M.Outlier-robust extreme learning machine for regression problems[J].Neurocomputing,2015,151:1519-1527.