Neural network for flight training intelligent evaluation

Abstract

Flight simulation and flight training data intelligent evaluation have been used wildly in pilots training. This paper is based on DirectX technology under a certain type of aircraft 3D model, implemented a type of aircraft flight simulation platform. Based on this platform, we simulated actual flight courses and obtained flight data from a special flight course. According to expertpsilas and special-class pilotpsilas flying experience, we extracted feature vectors as key parameters, and input them into a neural network model. After the neural network learning, more accurate flight evaluation results can be achieved. The algorithm greatly improves the efficiency of flight training data evaluation, reduces the man-made errors, corrects the deviation of the flight, and increases levels of pilotpsilas flight training. Considering slow convergence of BP neural network, calculated results affected by the initial value, poor stability, easy defects such as a local minimum, we applied L-M algorithm instead of gradient descent algorithm to neural network training. The establishment of the L-M algorithm based on the flight simulation data model has been developed. The research shows that results that are generated by L-M model are significantly better than the other three layers BP neural network models.