基于修正灰色残差算法的风廓线质量控制
|
摘要
为了研究时间域上风廓线数据的质量控制问题,采用后向传播神经网络修正灰色算法残差的方法(BP-GM),进行了理论分析和实验验证。使用反向传播神经网络训练历史风廓线数据的灰色残差,取得了风廓线质量控制数据。结果表明,当相对误差和后验差比值越小、精度越接近1时,质量控制效果越好;BP-GM法能有效地降低风廓线数据控制残差,提高精度。这一结果对风廓线质量控制是有帮助的。
In order to study quality control problem of wind profile data in time domain,a method of neural network was used to correct residual error of grey algorithm. Back propagation neural network was used to train grey residual error of historical wind profile data,and the quality control data of wind profile was obtained. After theoretical analysis and experimental verification,the results show that the algorithm can effectively reduce residual error and improve the accuracy of wind profile data. The result is helpful for quality control of wind profile.
In order to study quality control problem of wind profile data in time domain,a method of neural network was used to correct residual error of grey algorithm. Back propagation neural network was used to train grey residual error of historical wind profile data,and the quality control data of wind profile was obtained. After theoretical analysis and experimental verification,the results show that the algorithm can effectively reduce residual error and improve the accuracy of wind profile data. The result is helpful for quality control of wind profile.
引文
[1]CHEN Y,ZHOU B Zh,TAN J,et al.Research and application of airborne laser Doppler wind lidars[J].Laser Technology,2011,35(6):795-799(in Chinese).
[2]ZHANG H,ZHANG Y F.A prospective view on the application of Doppler wind-profiling radar[J].Meteorological,Hydrological and Marine Instruments,2001,18(1):12-16(in Chinese).
[3]ZHANG Y Y,GONG K,HE Sh F,et al.Progress in laser Doppler velocity measurement techniques[J].Laser&Infrared,2010,40(11):1157-1162(in Chinese).
[4]LI F,WU Y X,WEI A X.Review of reconstruction of 3-D building models based on airborne lidar[J].Laser Technology,2015,39(1):23-27(in Chinese).
[5]REN P,WANG Y L,KANG D Y,et al.Numerical optimization in VAD inversion technique for wind lidar[J].Laser Tcehnology,2009,33(6):664-666(in Chinese).
[6]HU Q,LI Y X,SONG J Z,et al.Application of Doppler LIDAR data in wind forecasting[J].Laser&Infrared,2012,42(3):268-273(in Chinese).
[7]ZHANG P F,LIU S,BI L,et al.Real-time WSR-88D velocity data quality control on bird detection[C]//Preprints of 31th Conference on Radar Meteorology.Seattle,USA:Meteor Society,2012:473-474.
[8]ZHU B,GAO Zh H.Spectral data processing of the wind-profile Radar[J].Modern Radar,2003,25(11):21-23(in Chinese).
[9]YU X D.The principle and application of Doppler weather radar[M].Beijing:Meteorological Press,2006:1-314(in Chinese).
[10]WANG Q M,GUO L L.Development of lidar in detection of low altitude wind shear[J].Laser&Infrared,2012,42(12):1324-1328(in Chinese).
[11]SHEN F H,SUN D S,WANG Zh Ch,et al.Beam scanning and wind inversion technique of a mobile Doppler lidar[J].Journal of Optics,2012,32(3):95-99(in Chinese).
[12]LUO R G,CHENG W.Improvement and application of grey model[J].Systems&Control Letters,1988,8(2):46-52(in Chinese).
[13]HSU C,CHEN C Y.Applications of improved grey prediction model for power demand forecasting[J].Energy Conversion&Management,2003,44(14):2241-2249.
[14]TAN G J.The structure method and application of background value in grey system GM(1,1)model(I)[J].Systems Engineering Theory&Practice,2000,20(5):125-127.
[15]LI J F,DAI W Zh.Research on the ameliorating GM(1,1)model and its application in the power quantity modeling of shanghai city[J].Systems Engineering&Theory Practice,2005,25(3):140-144(in Chinese).
[16]YANG W,NI Q D,WU J J.Research of the relation between the initial weight and convergence of BP neural network[J].Proceedings of EPSA,2002,14(1):20-22(in Chinese).
[17]LIU J,HUANG Y L.Nonlinear network traffic prediction based on BP neural network[J].Computer Applications,2007,27(7):1770-1772(in Chinese).
[18]SHEN H Y,WANG Zh X,GAO Ch Y,et al.Determining the number of BP neural network hidden layer units[J].Journal of Tianjin University of Technology,2008,24(5):13-15(in Chinese).
[19]WANG M L,WANG N P,LI X.Improvement and application of BPNN algorithm[J].Computer Engineering and Applications,2009,45(35):47-48(in Chinese).
[20]SU B,LIU L,YANG F T.Research of artificial neural network forecasting model based on grey relational analysis[J].Systems Engineering-Theory&Practice,2008,28(9):98-104(in Chinese).
[21]FAN H Y,FAN B Q,ZHANG L F.The application of gray-BP networks method in the passenger volume forecast[J].Journal of University of Shanghai for Science and Technology(Social Science Edition),2003,25(1):25-28(in Chinese).
[22]SHI H Y,KANG M N,DENG Zh H.Prediction model of urban construction land use based on grey BP network[J].Computer Engineering and Applications,2010,6(1):218-220(in Chinese).
[23]SI D M,LI L C,SONG J W.Power system load forecasting based upon combination of grey forecast and artificial neural network[J].Power System Technology,2001,25(12):14-17(in Chinese).
[2]ZHANG H,ZHANG Y F.A prospective view on the application of Doppler wind-profiling radar[J].Meteorological,Hydrological and Marine Instruments,2001,18(1):12-16(in Chinese).
[3]ZHANG Y Y,GONG K,HE Sh F,et al.Progress in laser Doppler velocity measurement techniques[J].Laser&Infrared,2010,40(11):1157-1162(in Chinese).
[4]LI F,WU Y X,WEI A X.Review of reconstruction of 3-D building models based on airborne lidar[J].Laser Technology,2015,39(1):23-27(in Chinese).
[5]REN P,WANG Y L,KANG D Y,et al.Numerical optimization in VAD inversion technique for wind lidar[J].Laser Tcehnology,2009,33(6):664-666(in Chinese).
[6]HU Q,LI Y X,SONG J Z,et al.Application of Doppler LIDAR data in wind forecasting[J].Laser&Infrared,2012,42(3):268-273(in Chinese).
[7]ZHANG P F,LIU S,BI L,et al.Real-time WSR-88D velocity data quality control on bird detection[C]//Preprints of 31th Conference on Radar Meteorology.Seattle,USA:Meteor Society,2012:473-474.
[8]ZHU B,GAO Zh H.Spectral data processing of the wind-profile Radar[J].Modern Radar,2003,25(11):21-23(in Chinese).
[9]YU X D.The principle and application of Doppler weather radar[M].Beijing:Meteorological Press,2006:1-314(in Chinese).
[10]WANG Q M,GUO L L.Development of lidar in detection of low altitude wind shear[J].Laser&Infrared,2012,42(12):1324-1328(in Chinese).
[11]SHEN F H,SUN D S,WANG Zh Ch,et al.Beam scanning and wind inversion technique of a mobile Doppler lidar[J].Journal of Optics,2012,32(3):95-99(in Chinese).
[12]LUO R G,CHENG W.Improvement and application of grey model[J].Systems&Control Letters,1988,8(2):46-52(in Chinese).
[13]HSU C,CHEN C Y.Applications of improved grey prediction model for power demand forecasting[J].Energy Conversion&Management,2003,44(14):2241-2249.
[14]TAN G J.The structure method and application of background value in grey system GM(1,1)model(I)[J].Systems Engineering Theory&Practice,2000,20(5):125-127.
[15]LI J F,DAI W Zh.Research on the ameliorating GM(1,1)model and its application in the power quantity modeling of shanghai city[J].Systems Engineering&Theory Practice,2005,25(3):140-144(in Chinese).
[16]YANG W,NI Q D,WU J J.Research of the relation between the initial weight and convergence of BP neural network[J].Proceedings of EPSA,2002,14(1):20-22(in Chinese).
[17]LIU J,HUANG Y L.Nonlinear network traffic prediction based on BP neural network[J].Computer Applications,2007,27(7):1770-1772(in Chinese).
[18]SHEN H Y,WANG Zh X,GAO Ch Y,et al.Determining the number of BP neural network hidden layer units[J].Journal of Tianjin University of Technology,2008,24(5):13-15(in Chinese).
[19]WANG M L,WANG N P,LI X.Improvement and application of BPNN algorithm[J].Computer Engineering and Applications,2009,45(35):47-48(in Chinese).
[20]SU B,LIU L,YANG F T.Research of artificial neural network forecasting model based on grey relational analysis[J].Systems Engineering-Theory&Practice,2008,28(9):98-104(in Chinese).
[21]FAN H Y,FAN B Q,ZHANG L F.The application of gray-BP networks method in the passenger volume forecast[J].Journal of University of Shanghai for Science and Technology(Social Science Edition),2003,25(1):25-28(in Chinese).
[22]SHI H Y,KANG M N,DENG Zh H.Prediction model of urban construction land use based on grey BP network[J].Computer Engineering and Applications,2010,6(1):218-220(in Chinese).
[23]SI D M,LI L C,SONG J W.Power system load forecasting based upon combination of grey forecast and artificial neural network[J].Power System Technology,2001,25(12):14-17(in Chinese).