To predict the Target Off-Block Time in time, accurately and stably, and improve the efficiency of cooperative operation of airports, airlines and air traffic controllers, a prediction method for prediction of flight off- block time based on BP neural network optimized by genetic algorithm optimization is proposed in this paper. Meanwhile, the influence of flight departure time on the prediction of Flight Off-Block Time is verified. The experimental data are divided by the departure time. The BP neural network optimized by genetic algorithm is applied to predict flight offblock time in the time before the arrival flight and in the time of flight entry. Compared with the traditional BP neural network prediction method and the empirical statistical prediction method, the experimental results show that the BP neural network optimized by genetic algorithm not only generates more stable prediction result, but also has higher prediction precision.
Flight Off-Block Time Prediction Based on BP Neural Network Optimized by Genetic Algorithm
20.10.2021
2024701 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
British Library Conference Proceedings | 2008
|Helicopter Sizing Based on Genetic Algorithm Optimized Neural Network
British Library Online Contents | 2006
|