Multilayer Feed Forward Neural Network Prediction Model For Court Case�s Time Span

2011 ◽  
Vol 6 (4) ◽  
pp. 13-26
Author(s):  
Behailu Getachew ◽  
2020 ◽  
Vol 305 ◽  
pp. 163-168
Author(s):  
Peng Gu ◽  
Chuan Min Zhu ◽  
Yin Yue Wu ◽  
Andrea Mura

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.


2017 ◽  
Vol 107 ◽  
pp. 206-211 ◽  
Author(s):  
Zhiyuan Liu ◽  
Xinyang Zhao ◽  
Jichao Sui ◽  
Hongli Wang ◽  
Yongcheng Liu ◽  
...  

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