An artificial neural network identification method for thermal resistance of exterior walls of buildings based on numerical experiments

2019 ◽  
Vol 12 (3) ◽  
pp. 425-440 ◽  
Author(s):  
Lin Chen ◽  
Changhong Zhan ◽  
Guanghao Li ◽  
Aimin Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Resistance ◽  
Numerical Experiments ◽  
Identification Method ◽  
Network Identification ◽  
Artificial Neural

The Tunnel Surrounding Rock Parameters Identification Method Based on PSO-ANN

2011 ◽  
Vol 94-96 ◽  
pp. 637-640 ◽  
Author(s):  
Zhan Ping Song ◽  
Song Bo Ren ◽  
Zhen Chao Guo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surrounding Rock ◽  
Parameters Identification ◽  
Soil Parameters ◽  
Data Set ◽  
Identification Method ◽  
Artificial Neural ◽  
Rock Parameters ◽  
Rock And Soil

Aiming at the complexity and uncertainty of rock and soil body, the paper proposed a tunnel surrounding rock parameters identification method combining numerical simulation, particle swarm optimization and artificial neural network. The method acquired data set between rock soil parameters and monitoring displacement and trained artificial neural network. The analytical theory and method are introduced in detail, analyzes the tunnel of Dalian Metro by the proposed method, and gets satisfied results. Which states that the parameters identification method based on PSO-ANN is feasible and has good foreground.

Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride

Nanoscale ◽  
2018 ◽  
Vol 10 (40) ◽  
pp. 19092-19099 ◽  
Author(s):  
Hong Yang ◽  
Zhongtao Zhang ◽  
Jingchao Zhang ◽  
Xiao Cheng Zeng
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Boron Nitride ◽  
Thermal Resistance ◽  
Hexagonal Boron Nitride ◽  
Machine Learning Algorithms ◽  
Single Layer Graphene ◽  
Interfacial Thermal Resistance ◽  
Artificial Neural

Several machine learning algorithms and artificial neural network structures are used to predict the interfacial thermal resistance between single layer graphene and hexagonal boron nitride with only the knowledge of the system temperature, inter-layer coupling strength, and in-plane tensile strain.

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