基于深度学习的地空导弹发射区拟合算法

At present, the fitting algorithm for ground-to-air missile launching areas mainly include the polynomial fitting algorithm and the Back Propagation (BP) neural network fitting method. The former is problematic in that the function form is difficult to determine, the function range is not easy to segment, and the fitting precision is low, whereas the latter requires a large number of hidden layer nodes to achieve higher precision. When the number of hidden layer nodes increases to a certain extent, its training becomes very difficult and its precision is difficult to keep improving. At the same time, traditional neural networks require a large amount of labeled data, which further increases the difficulty of practical applications. So, this paper designs a Stacked Sparse Auto-Encoder (SSAE) Deep Fitting Network (DFN) based on the deep learning theory, and provides the corresponding training strategy. The simulation results show that the proposed design has the advantage of getting lower fitting error compare to traditional methods. The deep sparse auto-encoder network designed in this paper can overcome the shortcomings of polynomial fitting and traditional neural network. This design can learn training with a large amount of unlabeled data and a small amount of tag data, further enhancing the ground-to-air missile launching area fitting accuracy.