A Low-Cost ANN Surrogate-Based Multi-Objective Optimization Method for Designing UWB Antennas

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198004" target="_blank" >RIV/00216305:26220/26:0198004 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11008382a" target="_blank" >https://ieeexplore.ieee.org/document/11008382a</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA65656.2025.11008382" target="_blank" >10.1109/RADIOELEKTRONIKA65656.2025.11008382</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Low-Cost ANN Surrogate-Based Multi-Objective Optimization Method for Designing UWB Antennas

Popis výsledku v původním jazyce

The design of antennas is a complex challenge that requires the simultaneous optimization of multiple performance metrics. Engineers must make tradeoffs between technical specifications, cost constraints, and practical implementation. This study investigates the application of multi-objective optimization techniques for the automated design and synthesis of ultra-wideband (UWB) monopole antenna systems. Specifically, it explores the balance between impedance matching and antenna miniaturization, ensuring optimal performance while minimizing physical footprint. The research leverages computational methods, incorporating Artificial Neural Network (ANN) surrogates trained on high-fidelity electromagnetic simulations to accelerate optimization. By integrating ANN-based surrogate modeling with evolutionary algorithms, this approach enhances computational efficiency and facilitates the rapid exploration of Pareto-optimal solutions.

Název v anglickém jazyce

A Low-Cost ANN Surrogate-Based Multi-Objective Optimization Method for Designing UWB Antennas

Popis výsledku anglicky

The design of antennas is a complex challenge that requires the simultaneous optimization of multiple performance metrics. Engineers must make tradeoffs between technical specifications, cost constraints, and practical implementation. This study investigates the application of multi-objective optimization techniques for the automated design and synthesis of ultra-wideband (UWB) monopole antenna systems. Specifically, it explores the balance between impedance matching and antenna miniaturization, ensuring optimal performance while minimizing physical footprint. The research leverages computational methods, incorporating Artificial Neural Network (ANN) surrogates trained on high-fidelity electromagnetic simulations to accelerate optimization. By integrating ANN-based surrogate modeling with evolutionary algorithms, this approach enhances computational efficiency and facilitates the rapid exploration of Pareto-optimal solutions.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceeding of the 35th International Conference Radioelektronika (RADIOELEKTRONIKA)

ISBN

979-8-3315-4447-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE

Místo vydání

Hnanice, Czech republic

Místo konání akce

Hnanice

Datum konání akce

12. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001509603700010