A Prediction Model of Hard landing Based on RBF Neural Network with K-means Clustering Algorithm

This paper proposes a prediction model for forecasting the hard landing problem. The landing phase has been demonstrated the most dangerous phase in flight cycle for fatal accidents. The landing safety problem has become one of the hot research problems in engineering management field. The study concentrates more on the prediction and advanced warning of hard landing. Firstly, flight data is preprocessed with data slicing method based on flight height and dimension reduction. Subsequently, the radial basis function (RBF) neural network model is established to predict the hard landing. Then, the structure parameters of the model are determined by the K-means clustering algorithm. In the end, compared with Support Vector Machine and BP neural network, the RBF neural network based on K-means clustering algorithm model is adopted and the prediction accuracy of hard landing is better than traditional ways.