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Novel interpretable mechanism of neural networks based on network decoupling method
Frontiers of Engineering Management 2021, Volume 8, Issue 4, Pages 572-581 doi: 10.1007/s42524-021-0169-x
Keywords: neural networks interpretability dynamical behavior network decouple
J. Sargolzaei, A. Hedayati Moghaddam
Frontiers of Chemical Science and Engineering 2013, Volume 7, Issue 3, Pages 357-365 doi: 10.1007/s11705-013-1336-3
Keywords: oil recovery artificial intelligence extraction neural networks supercritical extraction
Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE
Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3, Pages 609-622 doi: 10.1007/s11709-020-0623-6
Keywords: Artificial Neural Networks seismic vulnerability masonry buildings damage estimation vulnerability curves
T. Chandra Sekhara REDDY
Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 4, Pages 490-503 doi: 10.1007/s11709-017-0445-3
Keywords: artificial neural networks root mean square error SIFCON silica fume metakaolin steel fiber
S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG
Frontiers in Energy 2016, Volume 10, Issue 1, Pages 105-113 doi: 10.1007/s11708-016-0393-y
Keywords: day-ahead electricity markets price forecasting load forecasting artificial neural networks load serving
Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI
Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2, Pages 520-536 doi: 10.1007/s11709-021-0689-9
Keywords: unconfined compressive strength artificial neural network support vector machine predictive models regression
Yasser SHARIFI,Sajjad TOHIDI
Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 2, Pages 167-177 doi: 10.1007/s11709-014-0236-z
Keywords: steel I-beams lateral-torsional buckling finite element (FE) method artificial neural network (ANN) approach
Frontiers of Structural and Civil Engineering Pages 1213-1232 doi: 10.1007/s11709-022-0880-7
Keywords: FRCM deep neural networks confinement effect strength model confined concrete
Diffractive Deep Neural Networks at Visible Wavelengths Article
Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin
Engineering 2021, Volume 7, Issue 10, Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032
Keywords: Optical computation Optical neural networks Deep learning Optical machine learning Diffractive deepneural networks
Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD
Frontiers of Mechanical Engineering 2015, Volume 10, Issue 2, Pages 154-167 doi: 10.1007/s11465-015-0336-z
Keywords: information preview intelligent transportation state-of-charge trajectory builder immune systems artificial neural
A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks
Zhang Junyan,Feng Shouzhong,Liu Donghai
Strategic Study of Chinese Academy of Engineering 2005, Volume 7, Issue 10, Pages 87-90
Keywords: RBF neural networks tunnel construction surrounding rock deformation forecasting
Service life prediction of fly ash concrete using an artificial neural network
Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3, Pages 793-805 doi: 10.1007/s11709-021-0717-9
Keywords: concrete fly ash carbonation neural networks experimental validation service life
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 1, doi: 10.1007/s11783-023-1606-3
● Reducting the sampling frequency can enhance the modelling process.
Keywords: HDPE Pyrolysis Kinetics Thermogravimetric ANOVA Artificial neural network
Hydrogeological Parameter Identification Based on the Radial Basis Function Neural Networks
Zhang Junyan,Wei Lianwei,Han Weixiu,Shao Jingli,Cui Yali,Zhang Jianli
Strategic Study of Chinese Academy of Engineering 2004, Volume 6, Issue 8, Pages 74-78
Keywords: groundwater hydrogeological parameter radial basis function (RBF) neural networks BP neural networks
Learning and Applications of Procedure Neural Networks
He Xingui,Liang Jiuzhen,Xu Shaohua
Strategic Study of Chinese Academy of Engineering 2001, Volume 3, Issue 4, Pages 31-35
This paper deals with learning algorithms for procedure neural networks (PNN) and its applications
Keywords: procedure neural networks learning algorithm pattern recognition chemical reaction seepage
Title Author Date Type Operation
Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks
J. Sargolzaei, A. Hedayati Moghaddam
Journal Article
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for
Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE
Journal Article
Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network
T. Chandra Sekhara REDDY
Journal Article
Day-ahead electricity price forecasting using back propagation neural networks and weighted least square
S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG
Journal Article
Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support
Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI
Journal Article
Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks
Yasser SHARIFI,Sajjad TOHIDI
Journal Article
Development of deep neural network model to predict the compressive strength of FRCM confined columns
Journal Article
Diffractive Deep Neural Networks at Visible Wavelengths
Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin
Journal Article
immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy
Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD
Journal Article
A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks
Zhang Junyan,Feng Shouzhong,Liu Donghai
Journal Article
high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neuralnetworks
Journal Article
Hydrogeological Parameter Identification Based on the Radial Basis Function Neural Networks
Zhang Junyan,Wei Lianwei,Han Weixiu,Shao Jingli,Cui Yali,Zhang Jianli
Journal Article