Experimental Examination of the Behavior of Shotcrete-Reinforced Masonry Walls and Xgboost Neural Network Prediction Model

2021 ◽  
Author(s):  
Melda Alkan Çakıroğlu ◽  
Gülhan İnce ◽  
Hamide Tekeli Kabas ◽  
Ahmet Ali Süzen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Masonry Walls ◽  
Experimental Examination ◽  
Reinforced Masonry ◽  
Neural Network Prediction ◽  
Network Prediction

Energy Consumption Prediction Model of SiCp/Al Composite in Grinding Based on PSO-BP Neural Network

2020 ◽  
Vol 305 ◽  
pp. 163-168
Author(s):  
Peng Gu ◽  
Chuan Min Zhu ◽  
Yin Yue Wu ◽  
Andrea Mura
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Swarm Optimization ◽  
Al Composite ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

As the typical particle-reinforced aluminum matrix composite, SiCp/Al composite has low density, high elastic modulus and high thermal conductivity, and is one of the most competitive metal matrix composites. Grinding is the main processing technique of SiCp/Al composite, energy consumption of the grinding process provides guidance for the energy saving, which is the aim of green manufacturing. In this paper, grinding experiments were designed and conducted to obtain the energy consumption of the grinding machine tool. The Particle Swarm Optimization (PSO) BP neural network prediction model was applied in the energy consumption prediction model of SiCp/Al composite in grinding. It showed that the Particle Swarm Optimization (PSO) BP neural network prediction model has high prediction accuracy. The prediction model of energy consumption based on PSO-BP neural network is helpful in energy saving, which contributes to greening manufacturing.

scholarly journals PTZ Control System of Indoor Rail Inspection Robot Based on Neural Network Prediction Model

2017 ◽  
Vol 107 ◽  
pp. 206-211 ◽  
Author(s):  
Zhiyuan Liu ◽  
Xinyang Zhao ◽  
Jichao Sui ◽  
Hongli Wang ◽  
Yongcheng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Prediction Model ◽  
Inspection Robot ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Rail Inspection
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