Artificial neural network (ANN) approach for synthesis and optimization of (3D) three-dimensional periodic phased array antenna

Hamdi, Bilel; Selma, Limam; Aguili, Taoufik

doi:10.1109/antem.2016.7550118

Cited by 9 publications

(11 citation statements)

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“…Note that the electromagnetic calculation remains valid with some modification at the level of the excitation values. So, the numerical electromagnetic radiation pattern of the whole coupled almost periodic array can be presented as database content to build a feed-forward neural network training with supervised learning that approximates the following array pattern's function [13,16,17].…”

Section: −π Dymentioning

confidence: 99%

“…As we have previously seen in our previous work, this section explains how to conjoin the application of neural networks and almost periodic antenna array systems [13,16,17]. A neural network is a way to model any input to the output by establishing connections between their related radiation model data, especially if nothing is known about the model.…”

Section: Artificial Neural Network (Ann) Principlementioning

confidence: 99%

“…It's observed that the electromagnetic coupling is expressed by the lying of weak current surface on the non-exciting elements of the aperiodic configuration (which explains also the phenomenon of leaky waves). As shown in the figure (13), a Gibbs effect appears when the guide's modes number is not correctly reached the convergence level.…”

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confidence: 94%

“…Normally, the test set is just used to assess the performance of the network afterward. 60% of the data is used for training the network and the remaining 40% is used equally for validating and testing the network on unlearned inputs respectively [13,18]. Specifically, the application randomly divides the input vectors and target vectors into three known sets as follows: The input vectors and target vectors are divided into three known sets.…”

mentioning

confidence: 99%

“…The table (1) lists the training parameters of the artificial neural network (ANN) and the architectural parameters prepared for the implementation of the artificial neural network (ANN) models. [13,18]. After the training phase, the performance phases of the almost periodic array are accomplished to obtain the optimal weights for the new incoming signals.…”

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confidence: 99%

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

Hamdi¹,

Aguili²

2022

Preprint

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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.

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Section: −π Dymentioning

confidence: 99%

Section: Artificial Neural Network (Ann) Principlementioning

confidence: 99%

mentioning

confidence: 94%

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confidence: 99%

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confidence: 99%

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