Application of BP Neural Network Based on Genetic Algorithms Optimization in Prediction of Postgraduate Entrance Examination

2016 ◽  
Author(s):  
Li Chi ◽  
Li Lin
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Bp Neural Network ◽  
Entrance Examination

Research on Forecasting of Airflow Temperature in Heading Face Based on the GA-BP Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 668-672
Author(s):  
Xiang Zhang ◽  
Bai Shun Wang ◽  
Shuo Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
High Prediction ◽  
Bp Neural Network Model ◽  
Matlab Programming

In order to solve the problem of forecasting airflow temperature in heading face, a new model of forecasting airflow temperature in heading face with Matlab programming is built on the BP neural network model, making use of genetic algorithms to optimize the initial weights and thresholds of the network. According to the analysis of test carried out in a coal mine in Huainan, the results show that the model of fast convergence and high prediction accuracy is one of the most effective ways of forecasting airflow temperature in heading face.

Wind Power Output Prediction with BP Neural Network Combining Genetic Algorithms

2013 ◽  
Vol 860-863 ◽  
pp. 2526-2529 ◽  
Author(s):  
Xue Tao Pan ◽  
Ke Qing Qu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Wind Power ◽  
Bp Neural Network ◽  
Power Output ◽  
Back Propagation ◽  
Sample Analysis ◽  
Output Data ◽  
Bp Network ◽  
Wind Power Forecasting

While having been successfully applied in forecasting with many researches, back Propagation (BP) neural network are with problems such as permutation and premature convergence due to dependence on initial connection weights or other parameters. This paper investigates Genetic Algorithms (GA) evolved BP network and its application to wind power forecasting. Sample analysis with daily wind output data demonstrates that GA-based neural network is with better performance.

Modeling and Optimization Research of CFB-FGD Based on Improved Genetic Algorithms and BP Neural Network

2012 ◽  
Vol 610-613 ◽  
pp. 1601-1604 ◽  
Author(s):  
Zhi Bo Ren ◽  
Lei Sun ◽  
Yao Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Bp Neural Network ◽  
Flue Gas ◽  
Power Plants ◽  
Thermal Power ◽  
Flue Gas Desulfurization ◽  
Operating Costs ◽  
Thermal Power Plants ◽  
Bp Neural Network Model

In order to improve the efficiency of CFB-FGD (circulated fluidized bed for flue gas desulfurization) in many thermal power plants, this paper used the improved genetic algorithms and BP neural network to model and optimize the operation of CFB-FGD. First, this paper build BP neural network model to simulate CFB-FGD. Then, based on this model, we used the improved genetic algorithms to optimize CFB-FGD. The results can help improve the efficiency of CFB-FGD and decrease enterprise operating costs.

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

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator
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