Research On Performance Evaluation Of Air Traffic Management System Based On Improved SOM Neural Network

As a key hub for protecting the civil aviation industry,limited airspace resources and increasing flight volume have affected the Air Traffic Management system in civil aviation.The problems of low management efficiency and insufficient service quality are more prominent.In order to solve the above problems,and actively learn from the experience of foreign Air Traffic Management systems,it is necessary to establish a scientific and reasonable performance evaluation system and evaluation method.At the same time,this evaluation system must have their industry features.It can help to find the advantages and shortcomings of the current ATM system and optimize the management mode of China's ATM.Since the ATM system is a complex system involving three major systems,its performance evaluation research cannot use traditional evaluation methods.The research draws on the idea that the human brain can handle complex and high-dimensional information and data,and builds a set of PSO-SOM neural network-based performance evaluation methods for ATM systems based on factor analysis.The research process of this paper mainly has five aspects: First,the performance scope of the ATM system is defined.The second is to select the performance evaluation indicators of the ATM system based on the system management theory and the performance prism method.The third is to build a performance evaluation model based on SOM neural network,and optimize the model with factor analysis and particle swarm algorithm,and design the model algorithm program with MATLAB software.Four,it uses the performance evaluation model to study the performance evaluation of ATM systems in ten countries including China.The fifth is to conduct multi-dimensional analysis based on the results of performance evaluation.The empirical results show that using the model based can better reflect the performance level of the ATM system,and has strong feasibility and accuracy.