Information-Applied Technology in Neural Network Prediction Model of Aviation Unsafe Event Based on PSO Algorithm with Gradient Acceleration

2014 ◽  
Vol 952 ◽  
pp. 303-306 ◽  
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Jing Shun Duanmu

To improve the level of aviation safety management and decision-making, an aviation unsafe event model based on neural network trained by an improved Particle Swarm Optimization (PSO) algorithm is proposed. First a gradient acceleration idea is introduced in standard PSO algorithm to improve the search efficiency, and then the improved PSO algorithm is used to optimize the parameters of neural network, finishing the training of model. An actual example on unsafe event data of an airline shows that the proposed method can achieve a good prediction effect.

Author(s):  

In order to improve the feasibility and accuracy of the roadbed settlement prediction model, the factor analysis method is combined with the BP neural network method, and an improved BP neural network roadbed settlement prediction model is proposed. Select example data to test the improved BP neural network roadbed settlement prediction model. The test results: The relative average error of the 10 sets of training samples’ predicted and actual roadbed settlements was 4.287%, and the roads of five predicted samples The relative error of subgrade settlement is 1.79%, 1.93%, 6.62%, 7.19%, 4.05%, all less than 10%, which proves that the improved BP neural network prediction model has good prediction accuracy.


Author(s):  
Naohisa NISHIDA ◽  
Tatsumi OBA ◽  
Yuji UNAGAMI ◽  
Jason PAUL CRUZ ◽  
Naoto YANAI ◽  
...  

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