Application Research of BP Neural Network in English Teaching Evaluation

2013 ◽  
Vol 11 (8) ◽  
Author(s):  
Li Hongmei
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Teaching Evaluation ◽  
Application Research ◽  
English Teaching

Application Research on Improved Fusion Algorithm Based on BP Neural Network and POS

2015 ◽  
Vol 733 ◽  
pp. 898-901 ◽  
Author(s):  
Hong Li ◽  
Xue Ding
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Simulation Experiment ◽  
Solution Space ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Global Optimum ◽  
Application Research ◽  
Fusion Algorithm

Optimization problem is the problem which can be often encountered mostly in industrial design, and the key of optimization is to find the global optimum and higher constriction speed. This paper proposes a PSO algorithm based on BP neural network by neural network trains and selects individual extreme best randomly, to make the particle follow the optimal particle in the solution space search, and obtain the optimum extreme best in the whole situation. Through the application of the simulation experiment on image segmentation showed that the algorithm is suitable in dealing with multiple types function and constraint, with fast convergence speed, and easy combination with traditional optimization methods, thus improving its own limitations, and solving problems more efficiently.

BP Neural Network Application Research in Petrochemical Tower System Corrosion Prediction

2014 ◽  
Vol 488-489 ◽  
pp. 487-491 ◽  
Author(s):  
Yu Guang Fan ◽  
Min He ◽  
Hong Xian Lin ◽  
Bing Chen ◽  
San Ping Zhou
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Original Data ◽  
Training Data ◽  
Application Research ◽  
Fractionation Tower ◽  
Network Application ◽  
Standardized Method ◽  
Tower System

This paper takes the monitoring data sample from the top of fractionation tower system of one petrochemical company and uses prediction model which is constructed by BP neural network to study the corrosion prediction of catalytic fractionation tower top system. It uses min-max and z-score standardized method to deal with the original data and compare the impacts. The result shows that the BP neural constructing prediction model can provide basis of corrosion control for refinery. It also shows that better accuracy can be achieved by using min-max standardized method and when the number of training data quantity is over 20, the prediction result is more accurate and stable.

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