Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Qin, Qiong, Lai, Xu, Zou, Jin (2019) Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy. Applied Sciences, 9. 126pp. doi:10.3390/app9010126

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleDirect Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
JournalApplied Sciences
AuthorsQin, QiongAuthor
Lai, XuAuthor
Zou, JinAuthor
Year2019 (January 1)Volume9
PublisherMDPI AG
DOIdoi:10.3390/app9010126Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID10097817Long-form Identifiermindat:1:5:10097817:3
GUID0
Full ReferenceQin, Qiong, Lai, Xu, Zou, Jin (2019) Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy. Applied Sciences, 9. 126pp. doi:10.3390/app9010126
Plain TextQin, Qiong, Lai, Xu, Zou, Jin (2019) Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy. Applied Sciences, 9. 126pp. doi:10.3390/app9010126
In(n.d.) Applied Sciences Vol. 9. MDPI AG

See Also

These are possibly similar items as determined by title/reference text matching only.

Pan, Ziwei, Xu, Lei, Chen, Nengcheng (2025) Combining graph neural network and convolutional LSTM network for multistep soil moisture spatiotemporal prediction. Journal of Hydrology, 651. doi:10.1016/j.jhydrol.2024.132572
Yang, , Zhang, , Yang, , Lv, (2019) Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization. Applied Sciences, 9. 1794pp. doi:10.3390/app9091794
Qin, Huafeng, Wang, Peng (2019) Finger-Vein Verification Based on LSTM Recurrent Neural Networks. Applied Sciences, 9. 1687pp. doi:10.3390/app9081687
, , , , , (2018) Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals. Applied Sciences, 8. 185pp. doi:10.3390/app8020185
AbdelMoniem, , Gasser, , El-Mahallawy, , Fakhr, , Soliman, (2019) Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation. Applied Sciences, 9. 3022pp. doi:10.3390/app9153022
Meiping, Wang, Qi, Tian, Jiao, Zhang, Nana, Jin (2013) Application of a Hybrid Algorism Combining Fuzzy Theory and Neural Network for Heating Load Forecasting. Journal of Applied Sciences, 13. 1911-1915 doi:10.3923/jas.2013.1911.1915
Song, Hyunsun, Choi, Hyunjun (2023) Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models. Applied Sciences, 13 (7) 4644 doi:10.3390/app13074644
Crivellari, Alessandro, Beinat, Euro (2019) Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings. Applied Sciences, 9. 2861pp. doi:10.3390/app9142861
He, Caifen, Chen, Qiaote, Fang, Xuyuan, Zhou, Yangzhang, Fu, Randi, Jin, Wei (2021) Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network. Frontiers in Earth Science, 9. doi:10.3389/feart.2021.731803
Le, , Ho, , Lee, , Jung, (2019) Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water, 11 (7) 1387pp. doi:10.3390/w11071387
Cinaglia, Pietro, Cannataro, Mario (2022) Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation. Entropy, 24 (7) 929 doi:10.3390/e24070929
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 15, 2025 11:11:24
Go to top of page