Research on Ground-to-Air Missile Fitting Algorithm Based on Deep BP Neural Network

As one of the important parameters of the ground combat command system, it is necessary to determine the working equation of the surface-to-air missile launch area. However, at present, most of the fitting algorithms for surface-to-air missile launch area are still at the stage of polynomial fitting and traditional BP neural network fitting. Polynomial fitting has great limitations when facing such a complex problem as surface-to-air missile launch area, with poor fitting accuracy, while the traditional BP neural network can achieve high accuracy but it is difficult to further improve it. To address these problems, a depth fitting method based on BP neural network is proposed in this paper to further improve the fitting accuracy by increasing the number of hidden layers and the number of nodes in the hidden layers. Simulation experiments show that the method fits the surface-to-air missile launch area better than the traditional BP neural network, and not only the fitting error is lower, but also the improvement of fitting accuracy is very obvious.