Multigrade Artificial Neural Network for the Design of Finite Periodic Arrays

摘要

To solve the restriction of prior knowledge in artificial neural networks (ANNs) for the modeling of finite periodic arrays, a new multigrade ANN model is proposed in this paper. Considering mutual coupling and array environment, the proposed model is designed with two sub-ANNs, element-ANN and array-ANN. Based on the relationship between the geometrical variables and the electromagnetic (EM) behavior of elements in an array, element-ANN is built to provide prior knowledge for the modeling of array-ANN. Then, in a review of mutual coupling and array environment, array-ANN is modeled to obtain the EM response of the whole array from the nonlinear superposition of the element responses. Three numerical examples of a linear phased array, a six-element printed dipole array, and a U-slot microstrip array are employed to verify the effectiveness of the proposed model.