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For: Fu W, Wang K, Li C, Tan J. Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM. Energy Conversion and Management 2019;187:356-77. [DOI: 10.1016/j.enconman.2019.02.086] [Cited by in Crossref: 81] [Cited by in F6Publishing: 5] [Article Influence: 27.0] [Reference Citation Analysis]
Number Citing Articles
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5 Fu W, Wang K, Zhang C, Tan J. A hybrid approach for measuring the vibrational trend of hydroelectric unit with enhanced multi-scale chaotic series analysis and optimized least squares support vector machine. Transactions of the Institute of Measurement and Control 2019;41:4436-49. [DOI: 10.1177/0142331219860279] [Cited by in Crossref: 44] [Cited by in F6Publishing: 2] [Article Influence: 14.7] [Reference Citation Analysis]
6 Fu W, Wang K, Zhou J, Xu Y, Tan J, Chen T. A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy. Sustainability 2019;11:1804. [DOI: 10.3390/su11061804] [Cited by in Crossref: 27] [Cited by in F6Publishing: 5] [Article Influence: 9.0] [Reference Citation Analysis]
7 Sun W, Huang C. Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency. Journal of Cleaner Production 2022;338:130414. [DOI: 10.1016/j.jclepro.2022.130414] [Reference Citation Analysis]
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10 Fu W, Tan J, Zhang X, Chen T, Wang K. Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery. Complexity 2019;2019:1-17. [DOI: 10.1155/2019/3264969] [Cited by in Crossref: 37] [Article Influence: 12.3] [Reference Citation Analysis]
11 Sun W, Wang X, Tan B. Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network. Environ Sci Pollut Res. [DOI: 10.1007/s11356-022-19388-4] [Reference Citation Analysis]
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14 Fu W, Wang K, Tan J, Zhang K. A composite framework coupling multiple feature selection, compound prediction models and novel hybrid swarm optimizer-based synchronization optimization strategy for multi-step ahead short-term wind speed forecasting. Energy Conversion and Management 2020;205:112461. [DOI: 10.1016/j.enconman.2019.112461] [Cited by in Crossref: 48] [Cited by in F6Publishing: 7] [Article Influence: 24.0] [Reference Citation Analysis]
15 Devarapalli R, Bhattacharyya B. A hybrid modified grey wolf optimization‐sine cosine algorithm‐based power system stabilizer parameter tuning in a multimachine power system. Optim Control Appl Meth 2020;41:1143-59. [DOI: 10.1002/oca.2591] [Cited by in Crossref: 19] [Cited by in F6Publishing: 1] [Article Influence: 9.5] [Reference Citation Analysis]
16 Tan, Fu, Wang, Xue, Hu, Shan. Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy. Applied Sciences 2019;9:1676. [DOI: 10.3390/app9081676] [Cited by in Crossref: 12] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
17 Pei S, Qin H, Zhang Z, Yao L, Wang Y, Wang C, Liu Y, Jiang Z, Zhou J, Yi T. Wind speed prediction method based on Empirical Wavelet Transform and New Cell Update Long Short-Term Memory network. Energy Conversion and Management 2019;196:779-92. [DOI: 10.1016/j.enconman.2019.06.041] [Cited by in Crossref: 31] [Article Influence: 10.3] [Reference Citation Analysis]
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19 Chen X, Ding K, Zhang J, Han W, Liu Y, Yang Z, Weng S. Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM. Energy 2022;248:123574. [DOI: 10.1016/j.energy.2022.123574] [Reference Citation Analysis]
20 Peng T, Zhang C, Zhou J, Xia X, Xue X. Multi-Objective Optimization for Flood Interval Prediction Based on Orthogonal Chaotic NSGA-II and Kernel Extreme Learning Machine. Water Resour Manage 2019;33:4731-48. [DOI: 10.1007/s11269-019-02387-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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22 Ji T, Wang J, Li M, Wu Q. Short-term wind power forecast based on chaotic analysis and multivariate phase space reconstruction. Energy Conversion and Management 2022;254:115196. [DOI: 10.1016/j.enconman.2021.115196] [Reference Citation Analysis]
23 Sun W, Huang C. A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction. Environ Pollut 2020;266:115216. [PMID: 32763723 DOI: 10.1016/j.envpol.2020.115216] [Reference Citation Analysis]
24 Mao M, Zhou C, Yang J, Fang B, Liu F, Liu X, Salgotra R. Research on Fault Diagnosis Method of Rolling Bearing Based on Feature Optimization and Self-Adaptive SVM. Mathematical Problems in Engineering 2022;2022:1-20. [DOI: 10.1155/2022/6711019] [Reference Citation Analysis]
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26 Zhu S, Luo X, Yuan X, Xu Z. An improved long short-term memory network for streamflow forecasting in the upper Yangtze River. Stoch Environ Res Risk Assess 2020;34:1313-29. [DOI: 10.1007/s00477-020-01766-4] [Cited by in Crossref: 17] [Cited by in F6Publishing: 2] [Article Influence: 8.5] [Reference Citation Analysis]
27 Sannasi Chakravarthy S, Rajaguru H. Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning. IRBM 2021. [DOI: 10.1016/j.irbm.2020.12.004] [Cited by in Crossref: 5] [Article Influence: 5.0] [Reference Citation Analysis]
28 Niu W, Feng Z, Yang W, Zhang J. Short-term streamflow time series prediction model by machine learning tool based on data preprocessing technique and swarm intelligence algorithm. Hydrological Sciences Journal 2020;65:2590-603. [DOI: 10.1080/02626667.2020.1828889] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
29 Yin H, Ou Z, Huang S, Meng A. A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition. Energy 2019;189:116316. [DOI: 10.1016/j.energy.2019.116316] [Cited by in Crossref: 20] [Article Influence: 6.7] [Reference Citation Analysis]
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32 Lai X, Li C, Guo W, Xu Y, Li Y. Stability and dynamic characteristics of the nonlinear coupling system of hydropower station and power grid. Communications in Nonlinear Science and Numerical Simulation 2019;79:104919. [DOI: 10.1016/j.cnsns.2019.104919] [Cited by in Crossref: 18] [Article Influence: 6.0] [Reference Citation Analysis]