Consumption Prediction Model of Trenchless Repair Technology Based on BP Neural Network

2021 ◽  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Repair Technology ◽  
Consumption 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 Bayesian regularized NAR neural network based short-term prediction method of water consumption

2019 ◽  
Vol 118 ◽  
pp. 03024
Author(s):  
Jianyu Liu ◽  
Linxue Zhao ◽  
Yanlong Mao
Keyword(s):  
Neural Network ◽  
Water Supply ◽  
Prediction Model ◽  
Water Consumption ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Short Term ◽  
Neural Network Prediction ◽  
Short Term Prediction ◽  
Consumption Prediction

With the continuous construction of urban water supply infrastructure, it is extremely urgent to change the management mode of water supply from traditional manual experience to modern and efficient means. The water consumption forecast is the premise of water supply scheduling, and its accuracy also directly affects the effectiveness of water supply scheduling. This paper analyzes the regularity of water consumption time series, establishes a short-term water consumption prediction model based on Bayesian regularized NAR neural network, and compares and evaluates the prediction effect of the model. The verification results show that the Bayesian based NAR neural network prediction model has higher adaptability to the water consumption prediction than the standard BP neural network and the Bayesian regularized BP neural network. The prediction accuracy can more accurately reflect the short-term variation of water consumption.

Sign in / Sign up

Export Citation Format

Share Document