scholarly journals Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm

2018 ◽  
Vol 8 (5) ◽  
pp. 678 ◽  
Author(s):  
Shuyu Dai ◽  
Dongxiao Niu ◽  
Yan Li
Keyword(s):  
Support Vector Machine ◽  
Energy Consumption ◽  
Least Squares ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Shuffled Frog Leaping Algorithm ◽  
Mode Decomposition ◽  
Shuffled Frog Leaping

Short-Time Fluctuation Characteristic and Combined Forecasting of High-Speed Railway Passenger Flow Based on EEMD

2013 ◽  
Vol 409-410 ◽  
pp. 1071-1074
Author(s):  
Xiu Shan Jiang ◽  
Rui Feng Zhang ◽  
Liang Pan
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Mode Decomposition ◽  
Railway Passenger ◽  
Short Term Forecasting

Take Wuhan-Guangzhou high-speed railway for example. By adopting the empirical mode decomposition (EMD) attempt to analyze mode from the perspective of volatility of high speed railway passenger flow fluctuation signal. Constructed the ensemble empirical mode decomposition-gray support vector machine (EEMD-GSVM) short-term forecasting model which fuse the gray generation and support vector machine with the ensemble empirical mode decomposition (EEMD). Finally, by the accuracy of predicted results, explains the EEMD-GSVM model has the better adaptability.

Ensemble empirical mode decomposition-entropy and feature selection for pantograph fault diagnosis

2020 ◽  
Vol 26 (23-24) ◽  
pp. 2230-2242
Author(s):  
Ying Shi ◽  
Cai Yi ◽  
Jianhui Lin ◽  
Zhe Zhuang ◽  
Senhua Lai
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Effective Measure ◽  
Vibration Data ◽  
Ranking Criteria ◽  
Mode Decomposition

In this article, a fault diagnosis approach for a pantograph is developed with collected vibration data from a test rig. Ensemble empirical mode decomposition is used to decompose the signals to get intrinsic mode function, and four kinds of entropies (permu1tation entropy, approximate entropy, sample entropy, and fuzzy entropy) reflecting the working state are extracted as the inputs of the support vector machine based on particle swarm optimization algorithm support vector machine. The effect of data length, embedded dimension, and other parameters on calculation of the entropy value has also been studied. Multiple feature ranking criteria are used to select the useful features and improve the fault diagnosis accuracy of certain measurement points. Experimental results on pantograph vibration analysis have then confirmed that the proposed method provides an effective measure for pantograph diagnosis.

scholarly journals A Hybrid LSSVM Model with Empirical Mode Decomposition and Differential Evolution for Forecasting Monthly Precipitation

2017 ◽  
Vol 18 (1) ◽  
pp. 159-176 ◽  
Author(s):  
Lizhi Tao ◽  
Xinguang He ◽  
Rui Wang
Keyword(s):  
Support Vector Machine ◽  
Differential Evolution ◽  
Least Squares ◽  
Empirical Mode Decomposition ◽  
Superior Performance ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Forecast Performance ◽  
Statistical Measures ◽  
Mode Decomposition

Abstract In this study, a hybrid least squares support vector machine (HLSSVM) model is presented for effectively forecasting monthly precipitation. The hybrid method is designed by incorporating the empirical mode decomposition (EMD) for data preprocessing, partial information (PI) algorithm for input identification, and differential evolution (DE) for model parameter optimization into least squares support vector machine (LSSVM). The HLSSVM model is examined by forecasting monthly precipitation at 138 rain gauge stations in the Yangtze River basin and compared with the LSSVM and LSSVM–DE. The LSSVM–DE is built by combining the LSSVM and DE. Two statistical measures, Nash–Sutcliffe efficiency (NSE) and relative absolute error (RAE), are employed to evaluate the performance of the models. The comparison of results shows that the LSSVM–DE gets a superior performance to LSSVM, and the HLSSVM provides the best performance among the three models for monthly precipitation forecasts. Meanwhile, it is also observed that all the models exhibit significant spatial variability in forecast performance. The prediction is most skillful in the western and northwestern regions of the basin. In contrast, the prediction skill in the eastern and southeastern regions is generally low, which shows a strong relationship with the randomness of precipitation. Compared to LSSVM and LSSVM–DE, the proposed HLSSVM model gives a more significant improvement for most of the stations in the eastern and southeastern regions with higher randomness.

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