The Research of Flight Route Capacity in RVSM Airspace Based on Neural Network
Based on the qualitative analysis and research of implementing Reduce Vertical Separation Minimum (RVSM) to enhance the flight capacity in RVSM airspace all over the world, using systemic approaches that combine the qualitative analysis with ration analysis, this paper introduced mathematic description and neural network model of the air traffic issue in RVSM airspace. The mathematic description is set up to solve the super combinatorial optimization problem of air traffic capacity adjustment, and the artificial intelligence Neural Network model is set up based on air traffic flow modification as the flight level changed. It is brought forward that sorting and clustering idea combining with simulated annealing algorithm is to ameliorate time complexity. The optimal flight-level adjusting mode was obtained, which can improve the capacity of RVSM Airspace remarkably. The feasibility and validity are proved by the results of computer simulation, and it can be used to describe the fight route capacity enhancement during the RVSM implementation.
reduced vertical separation minimum (RVSM) Flight route capacity hopfield neural network sorting and clustering simulated annealing
Weijun Pan Huaqun Chen Tong chen
College of Computer Science Sichuan University Chengdu, China Air Traffic Management College Civil Aviation Flight University of China Guanghan, China
国际会议
太原
英文
525-528
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）