Multigrade Artificial Neural Network for the Design of Finite Periodic Arrays
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 arra...
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Published in | IEEE transactions on antennas and propagation Vol. 67; no. 5; pp. 3109 - 3116 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 0018-926X 1558-2221 |
DOI: | 10.1109/TAP.2019.2900359 |