Adoption of Ensemble Empirical Mode Decomposition Algorithm and Back Propagation Neural Network in Net Surface Solar Radiation Prediction

2020 ◽  
Vol 1651 ◽  
pp. 012174
Author(s):  
Yiting Wang ◽  
Wenan Tan ◽  
Yide Gong ◽  
Kai Guo ◽  
Shan Tang
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Empirical Mode Decomposition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
Decomposition Algorithm ◽  
Surface Solar Radiation ◽  
Mode Decomposition

scholarly journals Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Ocean Science ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 349-360 ◽  
Author(s):  
Zhiyuan Wu ◽  
Changbo Jiang ◽  
Mack Conde ◽  
Bin Deng ◽  
Jie Chen
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
The North ◽  
Mode Decomposition ◽  
Bpnn Model

Abstract. Sea surface temperature (SST) is the major factor that affects the ocean–atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble empirical mode decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each intrinsic mode function (IMF) has been taken as input data to the back-propagation neural network model. The final predicted SST data are obtained by aggregating the predicted data of individual series of IMFs (IMFi). A case study of the monthly mean SST anomaly (SSTA) in the northeastern region of the North Pacific shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on a BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction. Highlights include the following: Highlights. An SST-predicting method based on the hybrid EMD algorithms and BP neural network method is proposed in this paper. SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed. A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.

scholarly journals Hybrid Solar Forecasting Method Based on Empirical Mode Decomposition and Back Propagation Neural Network

2021 ◽  
Vol 231 ◽  
pp. 02001
Author(s):  
Ahmed Aghmadi ◽  
Soumia El Hani ◽  
Hamza Mediouni ◽  
Nisrin Naseri ◽  
Fatima El Issaoui
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Empirical Mode Decomposition ◽  
Back Propagation ◽  
Ensemble Methods ◽  
Back Propagation Neural Network ◽  
Solar Forecasting ◽  
Predictive Values ◽  
Mode Decomposition ◽  
Forecasting Method

In order to improve the accuracy of solar radiation prediction and optimize the energy management system. This study proposes a forecasting model based on empirical mode decomposition (EMD) and Back Propagation Neural Network (BPNN). Empirical mode of decomposition (EMD)-based ensemble methods with powerful predictive abilities have become relatively common in forecasting study. First, the existing solar radiation datasets are decomposed into an intrinsic mode function (IMF) and one residue produces fairly stationary sub-series that can easily be modeled on BPNN. Next, both components of the IMF and residue are applied to create the respective BPNN models. Then, the corresponding BPNN is used to predict some sub-series. Finally, the predictive values of the original solar radiation datasets are determined by the sum of each predicted sub-series. Compared with traditional models such as conventional neural network or ARIMA time series, the hybrid EMD-BPNN model shows great results in term of RMSE with 28.13 (W/m2). On the other hand, the result of BPNN and ARIMA was 83.28 (W/m2) and 108.88 (W/m2), respectively. that the non-stationary and non-linear of solar radiation signal has less effect on the accuracy of the prediction.

scholarly journals Hybrid improved EMD-BPNN model for the prediction of sea surface temperature

2018 ◽  
Author(s):  
Zhiyuan Wu ◽  
Changbo Jiang ◽  
Mack Conde ◽  
Bin Deng ◽  
Jie Chen
Keyword(s):  
Neural Network ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Mode Decomposition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
Sea Surface ◽  
Mode Decomposition ◽  
Bpnn Model

Abstract. Sea surface temperature (SST) is the major factor that affects the ocean-atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST predicting method based on improved empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. Ensemble empirical mode decomposition (EEMD) algorithm and Complementary Ensemble Empirical Mode Decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each Intrinsic Mode Function (IMF) has been taken as an input data to the back-propagation neural network model. The final predicted SST data is obtained by aggregating the predicted data of individual IMF. A case study, of the monthly mean sea surface temperature anomaly (SSTA) in the northeastern region of the North Pacific, shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction.

scholarly journals VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 228 ◽  
Author(s):  
Xiaobo Bi ◽  
Jiansheng Lin ◽  
Daijie Tang ◽  
Fengrong Bi ◽  
Xin Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Empirical Mode Decomposition ◽  
Fault Simulation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
K Value ◽  
Mode Decomposition

Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

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