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Effective forecast of Northeast Pacific sea surface temperature based on a complementary ensemble empirical mode decomposition–support vector machine method

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Atmospheric and Oceanic Science Letters 2017年第3期10卷 261-267页

作者： LI Qi-Jie ZHAO Ying LIAO Hong-Lin LI Jia-Kang Institute of Sciences University of Science and Technology of the Chinese People's Liberation Army

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.

关键词： Sea surface temperature complementary ensemble empirical mode decomposition support vector machine prediction

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Semi-supervised Dynamic Soft Sensor Based on complementary ensemble empirical mode decomposition and Gated Recurrent Units

Semi-supervised Dynamic Soft Sensor Based on Complementary E...

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第31届中国过程控制会议（CPCC2020）

作者： GUO Runyuan LIU Han XIE Guo ZHANG Youmin School of Automation and Information Engineering Xi’an University of Technology

[Objectives] Noise, redundancy and dynamic characteristics in complex industrial process data have been regarded as the key factors that affect the prediction accuracy of data-driven soft sensor models. In this paper, a semi-supervised dynamic soft sensor method is proposed to capture the dynamic characteristics of variable data while removing the noise and redundancy of the data to establish an accurate dynamic soft sensor prediction model. [Methods] First, auxiliary variable signals are decomposed effectively by complementary ensemble empirical mode decomposition(CEEMD), and the noise is eliminated based on the intrinsic mode functions(IMFs) obtained by the decomposition. Then, using the isometric feature mapping(Isomap) method to perform feature transformation of the remaining intrinsic mode functions, the variable data are reconstructed to remove the internal redundancy and noise. Finally, the preprocessed and serialized data are used to build a deep learning model based on a semi-supervised deep gated recurrent unit network aided multilayer perceptron(SSDGRU-MLP). To ensure effective training of the proposed SSDGRU-MLP model, callback functions and dropout technology are adopted. After the above offline procedures, a semi-supervised dynamic soft sensor prediction model can be obtained. Then, by importing the new serialized dataset into the established soft sensor model, the predicted values of the primary variable can be obtained through propagation over time and network depth. Since power plant air preheater is a dynamic system with complex and variable operating conditions, the semi-supervised dynamic soft sensor method proposed in this paper is used to predict rotor thermal deformation in the air preheater. So, the effectiveness and superiority of the new dynamic soft sensor are verified by this real industrial case. In our experiments, four auxiliary variables(gas temperature of the flue inlet, gas temperature of the flue outlet, air temperature of the air

关键词： dynamic soft sensor complementary ensemble empirical mode decomposition deep learning gated recurrent unit semi-supervised and dynamic modeling isometric feature mapping

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Denoising Surface Electromyography Signals Based on complementary ensemble empirical-mode decomposition and Multi-Scale Entropy

Denoising Surface Electromyography Signals Based on Compleme...

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第40届中国控制会议

作者： Rennong Wang Juan Zhao Zhentao Liu Feng Wang Jinhua She Graduate School of Engineering Tokyo University of Technology School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Surface electromyography（sEMG） can capture muscle activity and motor function,which is considered an effective tool for assessing local muscle *** to the complex environment and individual differences,the collected sEMG signals contain lots of noising information.A novel method is devised to effectively solve the problem of sEMG denoising based on complementary ensemble empirical mode decomposition（CEEMD） and multi-scale entropy（MSEn）.The method uses the concept of MSEn and analyzes the feature trend of MSEn of intrinsic mode functions（IMFs） obtained by *** method adaptively determines the main components of IMFs according to the complexity of sEMG on the time scale,avoiding insufficient selection based on *** experiments verified the effectiveness of the method.

关键词： Surface electromyography complementary ensemble empirical mode decomposition multi-scale entropy data processing

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Price prediction of power transformer materials based on CEEMD and GRU

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Global Energy Interconnection 2024年第2期7卷 217-227页

作者： Yan Huang Yufeng Hu Liangzheng Wu Shangyong Wen Zhengdong Wan Energy Development Research Institute China Southern Power GridGuangzhou 510623P.R.China China Southern Power Grid Guangzhou 510623P.R.China

The rapid growth of the Chinese economy has fueled the expansion of power *** transformers are key equipment in power grid projects,and their price changes have a significant impact on cost ***,the prices of power transformer materials manifest as nonsmooth and nonlinear ***,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering *** more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)***,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each ***,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU *** GRU model was used to directly predict the aggregation sequences with a small sample *** this study,we used authentic historical pricing data for power transformer materials to validate the proposed *** empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

关键词： Power transformer material Price prediction complementary ensemble empirical mode decomposition Gated recurrent unit empirical wavelet transform

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