Multigrade Artificial Neural Network for the Design of Finite Periodic Arrays

• Published in: IEEE transactions on antennas and propagation Vol. 67; no. 5; pp. 3109 - 3116
• Main Authors: Xiao, Li-Ye, Shao, Wei, Ding, Xiao, Liu, Qing Huo, Joines, William T.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrays; Artificial neural network (ANN); Artificial neural networks; Dipoles; Finite element analysis; Knowledge management; linear phased array; Mathematical models; Microstrip antenna arrays; Mutual coupling; Neural networks; Phased arrays; pole-residue-based transfer function (TF); six-element printed dipole array; Superposition (mathematics); Training; U-slot microstrip array
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2019.2900359

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.