Design and optimization of SIW patch antenna for Ku band applications using ANN algorithms

Chetioui, Mohammed; Boudkhil, Abdelhakim; Benabdallah, Nadia; Benahmed, Nadia

doi:10.1109/icoa.2018.8370530

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Antennas are one of the largest and rapidly evolving groups of microwave devices. For example, the substrate-integrated waveguide patch antenna, which works in the 12-18 GHz frequency range, is presented in [60]. The resonant frequency of the antenna is equal to 16.10 GHz.…”

Section: Antennasmentioning

confidence: 99%

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

et al. 2022

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The usage of techniques of the artificial neural networks (ANNs) in the field of microwave devices has recently increased. The advantages of ANNs in comparison with traditional full-wave methods are that the prediction speed when the traditional time-consuming iterative calculations are not required and also the complex mathematical model of the microwave device is no longer needed. Therefore, the design of microwave device could be repeated many times in real time. However, methods of artificial neural networks still lag behind traditional full-wave methods in terms of accuracy. The prediction accuracy depends on the structure of the selected neural network and also on the obtained dataset for the training of the network. Therefore, the paper presents a systematic review of the implementation of ANNs in the field of the design and analysis of microwave devices. The guidelines for the systematic literature review and the systematic mapping research procedure, as well as the Preferred Report Items for Systematic Reviews and Meta-Analysis statements (PRISMA) are used to conduct literature search and report the results. The goal of the paper is to summarize the application areas of usage of ANNs in the field of microwave devices, the type and structure of the used artificial neural networks, the type and size of the dataset, the interpolation and the augmentation of the training dataset, the training algorithm and training errors and also to discuss the future perspectives of the usage of ANNs in the field of microwave devices.

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Section: Antennasmentioning

confidence: 99%

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

et al. 2022

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“…ANNs were used to predict the geometrical parameters of a SIW patch antenna in Reference 149, taking as inputs the desired resonance frequency and the RL. Feed‐forward MLP and backpropagation were used for training the ANN in MATLAB, using dataset obtained from HFSS simulations.…”

Section: Predicting Antenna Parameters With Machine Learning Modelsmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

105

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This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

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“…Here, the back propagation learning algorithm [10]is used which is also called the gradient descent algorithm that provides the modification in w ij(k) in the form of weighted connection among neurons iand j in this way: ∆ = α + µ∆ (k -1)…………… (2) Where α denotes the learning coefficient, x j represent the input value, µ designates as the momentum coefficient, and belongs to a term depending on whether neuron iis a hidden neuron or a output neuron [11] [12]. In the training of neural network, gradient descent with adaptive learning rate algorithm is used and Kfold cross-validation is used for the test result to be more valuable .This method is used for finding the best ANN structural design.…”

Section: Optimization Using Neural Networkmentioning

confidence: 99%

Design and Optimization of Microstrip Patch Antenna using Artificial Neural Networks

2019

IJITEE

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In this paper a Neural Network model for the design of a Microstrip Patch Antenna for an Ultrawideband frequency range is presented. The reduced ground size is used to enhance bandwidth in proposed design. The results obtained from the proposed method are compared with the results of EM simulation software and are found to be in good agreement. The advantage of the proposed method lies with the fact that the various parameters required for the design of a Microstrip Patch Antenna at a particular frequency of interest can be easily extracted without going into the rigorous time consuming, iterative design procedures using a costly software package. In the paper staircase patch design is considered for ultra-wideband matching of Antenna. The results obtained from artificial neural network when compared with experimental and simulation results, found satisfactory and also it is concluded that Radial Basis Function (RBF) network is more accurate and fast for the proposed design.

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Design and optimization of SIW patch antenna for Ku band applications using ANN algorithms

Cited by 6 publications

References 9 publications

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

A review on the design and optimization of antennas using machine learning algorithms and techniques

Design and Optimization of Microstrip Patch Antenna using Artificial Neural Networks

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