Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Radiation Pattern Synthesis of the Coupled Almost Periodic Antenna Arrays Using Artificial Neural Network ANN Model

Version 1 : Received: 4 January 2022 / Approved: 6 January 2022 / Online: 6 January 2022 (09:30:59 CET)

How to cite: Bilel, H.; Taoufik, A. Radiation Pattern Synthesis of the Coupled Almost Periodic Antenna Arrays Using Artificial Neural Network ANN Model. Preprints 2022, 2022010048 (doi: 10.20944/preprints202201.0048.v1). Bilel, H.; Taoufik, A. Radiation Pattern Synthesis of the Coupled Almost Periodic Antenna Arrays Using Artificial Neural Network ANN Model. Preprints 2022, 2022010048 (doi: 10.20944/preprints202201.0048.v1).

Abstract

This paper proposes a radiation pattern synthesis of the almost periodic antenna arrays including mutual coupling effects (that extracted by the Floquet analysis according to our previous work), which principally has a high directivity and large bandwidth. For modeling the given structures, the moment method combined with the Generalized Equivalent Circuit (MoM-GEC) is proposed. The artificial neural network (ANN) as a powerful computational model has been successfully applied to the antenna array pattern synthesis. The results showed that the multilayer feedforward neural networks are rugged and can successfully and efficiently resolve various distinctive complex almost periodic antenna patterns (with different source amplitudes) (in particular, both periodic and randomly aperiodic structures are taken into account). However, the artificial neural network (ANN) is capable of quickly producing the synthesis results using generalization with the early stopping (ES) method. A significant time gain and memory consumption are achieved by using this given method to improve the generalization (called early stopping). To justify this work, several examples are developed and discussed.

Keywords

Radiation pattern synthesis; Almost periodic structures; Mutual coupling effects; Artificial neural network ANN algorithm; Early stoping method.

Subject

ENGINEERING, Electrical & Electronic Engineering

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.