Improving the Mechanical Property of Dissimilar Al/Mg Zn‐Added Ultrasound‐Assisted Friction Stir Lap Welding Joint by Back Propagation Neural Network–Gray Wolf Optimization Algorithm

2019 ◽  
Vol 21 (12) ◽  
pp. 1900973 ◽  
Author(s):  
Qi Song ◽  
Zhaoxu Ren ◽  
Shude Ji ◽  
Shiyu Niu ◽  
Weiwei Qi ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Property ◽  
Optimization Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Gray Wolf ◽  
Friction Stir Lap Welding ◽  
Friction Stir ◽  
Lap Welding ◽  
Ultrasound Assisted

scholarly journals A neural network-based prediction model in water monitoring networks

2021 ◽  
Author(s):  
Xiaohong Ji ◽  
Ying Pan ◽  
Guoqing Jia ◽  
Weidong Fang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ammonia Nitrogen ◽  
Quality Parameters ◽  
Water Monitoring ◽  
Monitoring Networks

Abstract To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm–back propagation (ISOA–BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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