These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

189 related articles for article (PubMed ID: 34712801)

  • 1. A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso.
    Wang T
    PeerJ Comput Sci; 2021; 7():e732. PubMed ID: 34712801
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting.
    Ren Y; Suganthan PN; Srikanth N
    IEEE Trans Neural Netw Learn Syst; 2016 Aug; 27(8):1793-8. PubMed ID: 25222957
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A hybrid approach for short-term forecasting of wind speed.
    Tatinati S; Veluvolu KC
    ScientificWorldJournal; 2013; 2013():548370. PubMed ID: 24453872
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Daily air quality index forecasting with hybrid models: A case in China.
    Zhu S; Lian X; Liu H; Hu J; Wang Y; Che J
    Environ Pollut; 2017 Dec; 231(Pt 2):1232-1244. PubMed ID: 28939124
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A hybrid prediction model for forecasting wind energy resources.
    Zhang Y; Pan G
    Environ Sci Pollut Res Int; 2020 Jun; 27(16):19428-19446. PubMed ID: 32215801
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A time series model based on hybrid-kernel least-squares support vector machine for short-term wind power forecasting.
    Ding M; Zhou H; Xie H; Wu M; Liu KZ; Nakanishi Y; Yokoyama R
    ISA Trans; 2021 Feb; 108():58-68. PubMed ID: 32958296
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Mid-long term forecasting of reservoir inflow using the coupling of time-varying filter-based empirical mode decomposition and gated recurrent unit.
    Wang X; Zhang S; Qiao H; Liu L; Tian F
    Environ Sci Pollut Res Int; 2022 Dec; 29(58):87200-87217. PubMed ID: 35804225
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Short-term wind speed prediction based on improved Hilbert-Huang transform method coupled with NAR dynamic neural network model.
    Chen J; Guo Z; Zhang L; Zhang S
    Sci Rep; 2024 Jan; 14(1):617. PubMed ID: 38182873
    [TBL] [Abstract][Full Text] [Related]  

  • 9. From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution.
    Shi S; Liu Z; Deng X; Chen S; Song D
    Sensors (Basel); 2023 Nov; 23(23):. PubMed ID: 38067752
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A novel model based on CEEMDAN, IWOA, and LSTM for ultra-short-term wind power forecasting.
    Yang S; Yuan A; Yu Z
    Environ Sci Pollut Res Int; 2023 Jan; 30(5):11689-11705. PubMed ID: 36098919
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network.
    Sun W; Wang X; Tan B
    Environ Sci Pollut Res Int; 2022 Jul; 29(33):49684-49699. PubMed ID: 35220530
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat algorithm.
    Cui Y; Huang C; Cui Y
    Environ Sci Pollut Res Int; 2020 Mar; 27(7):7353-7365. PubMed ID: 31884551
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer.
    Xinxin W; Xiaopan S; Xueyi A; Shijia L
    PLoS One; 2023; 18(9):e0289161. PubMed ID: 37682883
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Hybrid attention-based temporal convolutional bidirectional LSTM approach for wind speed interval prediction.
    Bommidi BS; Kosana V; Teeparthi K; Madasthu S
    Environ Sci Pollut Res Int; 2023 Mar; 30(14):40018-40030. PubMed ID: 36602735
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A four-stage hybrid model for hydrological time series forecasting.
    Di C; Yang X; Wang X
    PLoS One; 2014; 9(8):e104663. PubMed ID: 25111782
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.
    Chen Q; Wen D; Li X; Chen D; Lv H; Zhang J; Gao P
    PLoS One; 2019; 14(9):e0222365. PubMed ID: 31509599
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN.
    Akşehir ZD; Kılıç E
    PeerJ Comput Sci; 2024; 10():e1852. PubMed ID: 38435596
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Short-term wind speed prediction based on FEEMD-PE-SSA-BP.
    Zhu T; Wang W; Yu M
    Environ Sci Pollut Res Int; 2022 Nov; 29(52):79288-79305. PubMed ID: 35710968
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A hybrid wavelet transform based short-term wind speed forecasting approach.
    Wang J
    ScientificWorldJournal; 2014; 2014():914127. PubMed ID: 25136699
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Implementation of hybrid wind speed prediction model based on different data mining and signal processing approaches.
    Katipoğlu OM
    Environ Sci Pollut Res Int; 2023 May; 30(23):64589-64605. PubMed ID: 37071355
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.