中文版 | English

在结果中检索

CSpace

浏览/检索结果: 共17条，第1-10条 帮助

已选(0)清除 条数/页：   排序方式：
Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach 期刊论文
ENERGY, 2023, 卷号: 263, 页码: 16
作者:  Yang, Dongchuan;  Guo, Ju-e;  Li, Yanzhao;  Sun, Shaolong;  Wang, Shouyang
收藏  |  浏览/下载：57/0  |  提交时间：2023/02/07
Short -term load forecasting  Time series modeling  Dynamic decomposition-reconstruction tech  nique  Neural networks  
An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting 期刊论文
APPLIED ENERGY, 2022, 卷号: 306, 页码: 16
作者:  Yang, Dongchuan;  Guo, Ju-E;  Sun, Shaolong;  Han, Jing;  Wang, Shouyang
收藏  |  浏览/下载：118/0  |  提交时间：2022/04/02
Short-term load forecasting  Bivariate empirical mode decomposition  Decomposition-ensemble approach  Reconstruction  Bayesian optimization  Long short-term memory network  
Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm 期刊论文
RENEWABLE ENERGY, 2020, 卷号: 162, 页码: 1208-1226
作者:  Hu, Jianming;  Heng, Jiani;  Wen, Jiemei;  Zhao, Weigang
收藏  |  浏览/下载：139/0  |  提交时间：2021/04/26
Renewable energy  Complete empirical mode decomposition with adaptive noise  Quantile regression neural network  Wind speed forecasting  Distance correlation  
Forecasting container throughput based on wavelet transforms within a decomposition-ensemble methodology: a case study of China 期刊论文
MARITIME POLICY & MANAGEMENT, 2019, 卷号: 46, 期号: 2, 页码: 178-200
作者:  Xie, Gang;  Qian, Yatong;  Yang, Hewei
收藏  |  浏览/下载：165/0  |  提交时间：2019/03/05
Container throughput  wavelet transform  data characteristic analysis  time series forecasting  decomposition-ensemble methodology  
A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration 期刊论文
ATMOSPHERIC POLLUTION RESEARCH, 2018, 卷号: 9, 期号: 6, 页码: 989-999
作者:  Gan, Kai;  Sun, Shaolong;  Wang, Shouyang;  Wei, Yunjie
收藏  |  浏览/下载：252/0  |  提交时间：2018/11/16
Secondary-decomposition-ensemble learning paradigm  Complementary ensemble empirical mode decomposition  Phase space reconstruction  Least square support vector regression  Hybrid intelligent algorithm  
A semi-heterogeneous approach to combining crude oil price forecasts 期刊论文
INFORMATION SCIENCES, 2018, 卷号: 460, 页码: 279-292
作者:  Wang, Jue;  Li, Xiang;  Hong, Tao;  Wang, Shouyang
收藏  |  浏览/下载：235/0  |  提交时间：2018/10/07
Crude oil price  Decomposition and reconstruction  Forecast combination  Model selection  
Simultaneous identification of diffusion coefficient, spacewise dependent source and initial value for one-dimensional heat equation 期刊论文
MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 2017, 卷号: 40, 期号: 10, 页码: 3552-3565
作者:  Zhao, Zhi-Xue;  Banda, Mapundi K.;  Guo, Bao-Zhu
浏览  |  Adobe PDF(475Kb)  |  收藏  |  浏览/下载：540/166  |  提交时间：2018/07/30
Inverse Problem  Matrix Pencil Method  Finite Difference Method  Truncated Singular Value Decomposition  Generalized Cross-validation  
Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting 期刊论文
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2017, 卷号: 196, 页码: 110-118
作者:  Niu, Mingfei;  Gan, Kai;  Sun, Shaolong;  Li, Fengying
收藏  |  浏览/下载：103/0  |  提交时间：2018/07/30
PM2.5 concentration forecasting  Decomposition-ensemble learning paradigm  EEMD  PSR  LSSVM  
A new approach for crude oil price analysis based on Empirical Mode Decomposition 期刊论文
ENERGY ECONOMICS, 2008, 卷号: 30, 期号: 3, 页码: 905-918
作者:  Zhang, Xun;  Lai, K. K.;  Wang, Shou-Yang
收藏  |  浏览/下载：96/0  |  提交时间：2018/07/30
Empirical Mode Decomposition  crude oil price  forecasting  composition  volatility  
On direct methods for time-limited signal and image reconstruction and enhancement 期刊论文
INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2007, 卷号: 5, 期号: 1, 页码: 51-68
作者:  Wang, Yanfei;  Wen, Zaiwen;  Nashed, Zuhair;  Sun, Qiyu
收藏  |  浏览/下载：113/0  |  提交时间：2018/07/30
signal reconstruction  discrete Fourier transform  singular value decomposition  signal and image enhancement