Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

Lahiani, Mohamed Aziz; Raida, Zbyněk; Veselý, Jiří; Olivová, Jana

doi:10.3390/electronics12061345

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The trained model can accurately reflect the output responses of the modeled device. The more data that provide a nonlinear relationship within a certain range, the more accurate the prediction of the ANNs will be [ 24 , 25 ]. To avoid the large amount of data required for ANNs, Neuro-SM is proposed for modeling microwave devices [ 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines

Yan

Qian

et al. 2023

Micromachines

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In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), including both geometric variables and frequency variables to improve accuracy with fewer variables. Additionally, an advanced method incorporating simple sensitivity analysis expressions into the training process is proposed to accelerate the optimization process. The experimental results show that the proposed model with its simple structure and an effective training process can accurately reflect the performance of coupled microstrip lines. The proposed model is more compatible than models in existing simulation software.

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Section: Introductionmentioning

confidence: 99%

Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines

Yan

Qian

et al. 2023

Micromachines

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“…They are exploited by global search procedures [60] and multi-criterial optimization [61] and frequently combined with sequential sampling routines [62,63]. Their advantages are simplicity of implementation, flexibility, and abundance of the available techniques [64][65][66][67]. Disadvantages are associated with the curse of dimensionality having a strong adverse effect on the modeling process in terms of the number of designable variables that may be taken into consideration, as well as their ranges.…”

Section: Introductionmentioning

confidence: 99%

Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement

Pietrenko-Dabrowska,

Koziel,

Zhang

2023

Electronics

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A variety of surrogate modeling techniques has been utilized in high-frequency design over the last two decades. Yet, the curse of dimensionality still poses a serious challenge in setting up reliable design-ready surrogates of modern microwave components. The difficulty of the modeling task is only aggravated by nonlinearity of circuit responses. Consequently, constructing a practically usable surrogate model, valid across extended ranges of material, geometry, and operational parameters, is far from easy. As a matter of fact, conventional modeling techniques are merely capable of building models for microwave structures featuring a relatively small number of designable parameters within reduced ranges thereof. One possible way of mitigating these obstacles may be the employment of the recently proposed two-stage performance-driven modeling approach. Therein, the surrogate model domain is narrowed down to the section of the space where the vectors of adequate quality are located, thereby permitting significantly reducing the cost of acquiring the training data. Seeking even further cost reduction, this work introduces a novel modeling framework, which exploits problem-specific knowledge extracted from the circuit responses to achieve substantial cost-savings of training data acquisition. In our methodology, the modeling procedure targets response features instead of the complete responses. The response features are the characteristic locations of the circuit response, such as relevant minima or maxima over selected frequency bands. The dependency of the coordinates of the said features on circuit dimensions is considerably less nonlinear than is observed for the complete characteristics, which enables sizable reduction of the data acquisition cost. Numerical validation of our procedure involving three microwave structures corroborates its remarkable efficiency, which allows for setting design-ready surrogates using only a handful of samples.

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Meander Structure Analysis Techniques Using Artificial Neural Networks

Belova-Plonienė,

Krukonis,

Abromavičius

et al. 2024

Applied Sciences

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Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to perform a preliminary study of the designed structures. This study aims to explore the application of neural networks in the design and analysis of meander structures. Three different feedforward neural network (FFNN), time delay neural network (TDNN), and convolutional neural network (CNN) techniques were investigated for the analysis and design of the meander structures in this article. The geometric dimensions or top-view images of 369 different meander structures were used for training an FFNN, TDNN, and CNN. The investigated networks were designed to predict such electrodynamic parameters as the delay time (td), reflection coefficient (S11), and transmission coefficient (S21) in the 0–10 GHz frequency band. A sufficiently low mean absolute error (MAE) was achieved with all three methods for the analysis of MSs. Using an FFNN, the characteristic td was predicted with a 3.3 ps average MAE. The characteristic S21 was predicted with a 0.64 dB average MAE, and S11 was predicted with a 2.47 dB average MAE. The TDNN allowed the average MAEs to be reduced to 0.9 ps, 0.11 dB, and 1.63 dB, respectively. Using a CNN, the average MAEs were 27.5 ps, 0.44 dB, and 1.36 dB, respectively. The use of neural networks has allowed accelerating the analysis procedure from approximately 120 min on average to less than 5 min.

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Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

Cited by 3 publications

References 21 publications

Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines

Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines

Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement

Meander Structure Analysis Techniques Using Artificial Neural Networks

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