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Research on Anti-noise Processing Method of Production Signal Based on Ensemble Empirical Mode Decomposition(Eemd)

Journal of Northeast Agricultural University(English Edition) 2017年第4期24卷 69-79页

作者： Fang Jun-long Yu Xiao-juan Wang Rui-fa Wang Run-tao Li Peng-fei Shao Chang-hui College of Electrical and Information Northeast Agricultural University State Grid corporation of Heilongjiang Electric Power Co. Ltd.

The grain production prediction is one of the most important links in precision agriculture. In the process of grain production prediction, mechanical noise caused by the factors of difference in field topography and mechanical vibration will be mixed in the original signal, which undoubtedly will affect the prediction accuracy. Therefore, in order to reduce the influence of vibration noise on the prediction accuracy, an adaptive Ensemble Empirical Mode Decomposition(Eemd) threshold filtering algorithm was applied to the original signal in this paper: the output signal was decomposed into a finite number of Intrinsic Mode Functions(IMF) from high frequency to low frequency by using the Empirical Mode Decomposition(emd) algorithm which could effectively restrain the mode mixing phenomenon; then the demarcation point of high and low frequency IMF components were determined by Continuous Mean Square Error criterion(CMSE), the high frequency IMF components were denoised by wavelet threshold algorithm, and finally the signal was reconstructed. The algorithm was an improved algorithm based on the commonly used wavelet threshold. The two algorithms were used to denoise the original production signal respectively, the adaptive Eemd threshold filtering algorithm had significant advantages in three denoising performance indexes of signal denoising ratio, root mean square error and smoothness. The five field verification tests showed that the average error of field experiment was 1.994% and the maximum relative error was less than 3%. According to the test results, the relative error of the predicted yield per hectare was 2.97%, which was relative to the actual yield. The test results showed that the algorithm could effectively resist noise and improve the accuracy of prediction.

关键词： production signal signal denoising processing adaptive Eemd threshold filtering algorithm prediction accuracy

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A Hybrid Approach for Studying the Lead-Lag Relationships Between China’s Onshore and Offshore Exchange Rates Considering the Impact of Extreme Events

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Journal of Systems Science & Complexity 2018年第3期31卷 734-749页

作者： WEI Yunjie WEI Qi WANG Shouyang LAI Kin Keung Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Center for Forecasting Science Chinese Academy of Sciences Beijing 100190 China Department of Management Sciences City University of Hong Kong Hong Kong China School of Finance Central University of Finance and Economics Beijing 100081 China China Great Wall Asset Management corporation Beijing 100045 China International Business School Shaanxi Normal University Xi'an 710119 China Department of Industrial and Manufacturing Systems Engineering Hong Kong University Hong Kong China

Understanding the characteristics of the dynamic relationship between the onshore Renminbi (CNY) and the offshore Renminbi (CNH) exchange rates considering the impact of some extreme events is very important and it has wide implications in several areas such as hedging. For better estimating the dynamic relationship between CNY and CNH, the Granger-causality test and Bry-Boschan Business Cycle Dating Algorithm were employed in this paper. Due to the intrinsic complexity of the lead-lag relationships between CNY and CNH, the empirical mode decomposition (emd) algorithm is used to decompose those time series data into several intrinsic mode function (IMF) components and a residual sequence, from high to low frequency. Based on the frequencies, the IMFs and a residual sequence are combined into three components, identified as short-term composition caused by some market activities, medium-term composition caused by some extreme events and the long-term trend. The empirical results indicate that when it only matters the short-term market activities, CNH always leads CNY; while the medium-term impact caused by those extreme events may alternate the lead-lag relationships between CNY and CNH.

关键词：极端事件汇率近海中国学习混合动态关系模式分解

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