scholarly journals Vehicle Interior Sound Quality Prediction Based on Back Propagation Neural Network

2011 ◽  
Vol 11 ◽  
pp. 471-477 ◽  
Author(s):  
Gang-Ping Tan ◽  
Deng-Feng Wang ◽  
Qian Li
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Sound Quality ◽  
Back Propagation Neural Network ◽  
Quality Prediction ◽  
Vehicle Interior

Yarn Quality Prediction and Process Parameters Optimization Based on Genetic Algorithm and Neural Network

2015 ◽  
Vol 742 ◽  
pp. 377-383
Author(s):  
Jian Guo Yang ◽  
Lan Xu ◽  
Zhi Jun Lu ◽  
Qian Xiang ◽  
Bin Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Process Parameters ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Parameters Optimization ◽  
Model Parameters ◽  
Quality Prediction ◽  
Yarn Quality ◽  
Important Means

Quality prediction is an important means of the quality management in modern spinning production. This paper proposed a yarn quality prediction model based on Genetic Algorithm and back propagation neural network to predict the yarn quality and optimize the process parameters. The main identification model parameters were optimized by using genetic algorithm, and the prediction performance of the model has been compared against that of the BP neural network model. The effectiveness and availability of the proposed model are verified with the use of actual production data.

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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