The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST...The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition （CEEMD） and support vector machine （SVM） methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3℃ and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments.展开更多
To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT...To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT signal affected by strong interference.First,the approximate entropy,fuzzy entropy,sample entropy,and Lempel-Ziv(LZ)complexity are extracted from the magnetotelluric data.Then,four robust parameters are used as the inputs to the support vector machine(SVM)to train the sample library and build a model based on the different complexity of signals.Based on this model,we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition(CEEMD)and wavelet threshold(WT)for noise suppression.Simulation results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise suppression.Compared with the CEEMD WT,the proposed SVM-CEEMDWT method retains more low-frequency low-variability information,and the apparent resistivity curve is smoother and more continuous.Moreover,the results better reflect the deep electrical structure in the field.展开更多
To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is pr...To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is proposed.Firstly,EMD is employed to decompose the raw drift series into a finite number of intrinsic mode functions(IMFs)with the frequency descending successively.Secondly,according to the time-frequency characteristic of each IMF,the corresponding SVR prediction model is established based on phase space reconstruction.Finally,the prediction results are obtained by adding up the prediction results of all IMFs with equal weight.The experimental results demonstrate the validity of the proposed model in random drift prediction of MEMS gyroscope.Compared with a single SVR model,the proposed model has higher prediction precision,which can provide the basis for drift error compensation of MEMS gyroscope.展开更多
基金supported in part by the Major Research Plan of the National Natural Science Foundation of China[grant number91530204]the State Key Program of the National Natural Science Foundation of China[grant number 41430426]
文摘The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition （CEEMD） and support vector machine （SVM） methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3℃ and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments.
基金funded by the National Key R&D Program of China(No.2018YFC0603202)the National Natural Science Foundation of China(No.41404111)+1 种基金Natural Science Foundation of Hunan Province(No.2018JJ2258)Hunan Provincial Science and Technology Project Foundation(No.2018TP1018)
文摘To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT signal affected by strong interference.First,the approximate entropy,fuzzy entropy,sample entropy,and Lempel-Ziv(LZ)complexity are extracted from the magnetotelluric data.Then,four robust parameters are used as the inputs to the support vector machine(SVM)to train the sample library and build a model based on the different complexity of signals.Based on this model,we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition(CEEMD)and wavelet threshold(WT)for noise suppression.Simulation results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise suppression.Compared with the CEEMD WT,the proposed SVM-CEEMDWT method retains more low-frequency low-variability information,and the apparent resistivity curve is smoother and more continuous.Moreover,the results better reflect the deep electrical structure in the field.
基金National Natural Science Foundation of China(No.61427810)。
文摘To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is proposed.Firstly,EMD is employed to decompose the raw drift series into a finite number of intrinsic mode functions(IMFs)with the frequency descending successively.Secondly,according to the time-frequency characteristic of each IMF,the corresponding SVR prediction model is established based on phase space reconstruction.Finally,the prediction results are obtained by adding up the prediction results of all IMFs with equal weight.The experimental results demonstrate the validity of the proposed model in random drift prediction of MEMS gyroscope.Compared with a single SVR model,the proposed model has higher prediction precision,which can provide the basis for drift error compensation of MEMS gyroscope.
