Wavelet Network Modeling Method Based on Radiation Characteristics of Array Antenna

Abstract

In complex electromagnetic environment, the training data contains a lot of noise, which can make classic neural networks overfit easily. In order to solve this problem, a new method based on sampling theory is proposed for constructing models. Through analyzing wavelet networks and radiation fields in the frequency domain, it is proved that the new method can effectively reduce noise on models and help wavelet networks obtain a good generalization. In the simulation, the new method is compared with regularization techniques. The results show that the new method can prevent networks from overfitting and is more robust to noise.