Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element. (21st October 2019)

• Record Type: Journal Article
• Authors: Mahouti, Peyman
• Other Names: Wang Chao‐Fu guestEditor.
• Is Part Of: International journal of numerical modelling. Volume 33:Number 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-10-21
• Subjects: convolutional neural network -- deep learning -- reflectarray antenna -- regression -- symbolic regression; Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2689
• Languages: English
• ISSNs: 0894-3370

Abstract

Abstract: Reflectarray antennas (RAs) have the ability to combine the advantages of both traditional parabolic reflector and phased array antennas without the need for feed network designs. Microstrip reflectarrays (MRAs) have the advantages of being small size, light weighted, easy to prototyped, high gain, low side‐lobe level, and a predetermined radiation pattern. These can be achieved by precise calculation of reflection phase at each RA unit independently with a phase compensation proportional to the distance from the feed. The challenging problem is to have a fast and high accurate unit element to be used in multidimension, multiobjective design optimization. Herein, artificial intelligence algorithms (AIAs) have been used for prediction of reflection phase characterization of an X band MRA unit element with respect to the geometrical design parameters. Firstly, a nonuniform unit RA has been designed in 3D electromagnetic (EM) simulation tool for creating the training validation data sets. Then, the data sets are given to the different types of AIA regression models such as multilayer perceptron, symbolic regression, and convolutional neural network. From the results of the validation data set, it can be concluded that the proposed models have sufficient accuracy that can be used in a computationally efficient design optimization process of a large‐scale RA design.