المؤلفون: Huayang Li, Qiang Tan, Bojia Li, Yongcun Feng, Baohong Dong, Ke Yan, Jianqi Ding, Shuiliang Zhang, Jinlong Guo, Jingen Deng, Jiaao Chen

المصدر: Applied Sciences ; Volume 13 ; Issue 18 ; Pages: 10147

مصطلحات موضوعية: leakage pressure, long short-term memory backpropagation (LSTM-BP) neural network, lost circulation, deep learning, correlation analysis

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: Earth Sciences and Geography; https://dx.doi.org/10.3390/app131810147

الاتاحة: https://doi.org/10.3390/app131810147

المؤلفون: He Xu, Bin Lv, Jie Chen, Lei Kou, Hailin Liu, Min Liu

المصدر: Water; Volume 15; Issue 15; Pages: 2760

مصطلحات موضوعية: prediction of water quality, marine ranching, deep learning, LSTM-BP combined neural network

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: New Sensors, New Technologies and Machine Learning in Water Sciences; https://dx.doi.org/10.3390/w15152760

الاتاحة: https://doi.org/10.3390/w15152760

المؤلفون: Xiaoya Ma, Mengxiu Li, Jin Tong, Xiaying Feng

المصدر: Biomimetics; Volume 8; Issue 3; Pages: 312

مصطلحات موضوعية: demand forecasting, prediction modeling, deep learning, intelligent supply chain, new energy vehicles, SARIMA-LSTM-BP model

وصف الملف: application/pdf

Relation: Biological Optimisation and Management; https://dx.doi.org/10.3390/biomimetics8030312

الاتاحة: https://doi.org/10.3390/biomimetics8030312

المؤلفون: Liang Huang, Shijie Chen, Zaixun Ling, Yibo Cui, Qiong Wang

المصدر: Energy Reports, Vol 7, Iss , Pp 485-492 (2021)

مصطلحات موضوعية: Load identification, PCA, LSTM-BP, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S235248472100041X; https://doaj.org/toc/2352-4847

URL الوصول: https://doaj.org/article/0a86abaf7ded47cf99f81ad454161598

المؤلفون: Wenchao Ban, Liangduo Shen

المصدر: Sustainability; Volume 14; Issue 23; Pages: 16128

مصطلحات موضوعية: timeseries data, PM2.5 concentration prediction, CEEMDAN–LSTM–BP–ARIMA coupling model

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: https://dx.doi.org/10.3390/su142316128

الاتاحة: https://doi.org/10.3390/su142316128

المؤلفون: Shijie Chen, Qiong Wang, Yi-bo Cui, Zai-xun Ling, Liang Huang

المصدر: Energy Reports, Vol 7, Iss, Pp 485-492 (2021)

مصطلحات موضوعية: PCA, Artificial neural network, LSTM-BP, Event (computing), Computer science, 020209 energy, Stability (learning theory), Load identification, 02 engineering and technology, computer.software_genre, Power (physics), Data set, Identification (information), Smart power, General Energy, 020401 chemical engineering, Dimension (vector space), 0202 electrical engineering, electronic engineering, information engineering, Data mining, lcsh:Electrical engineering. Electronics. Nuclear engineering, 0204 chemical engineering, computer, lcsh:TK1-9971

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a3b0f98e7aa80a880331b1000706817
http://www.sciencedirect.com/science/article/pii/S235248472100041X