МОДЕЛИРОВАНИЕ И ОПТИМИЗАЦИЯ ХАРАКТЕРИСТИК МИКРОПОЛОСКОВЫХ ПАТЧ-АНТЕНН НА ОСНОВЕ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ
DOI:
https://doi.org/10.52167/1609-1817-2025-1-S1-474-489Ключевые слова:
микрополосковая патч-антенна, машинное обучение, интеллектуальная оптимизация, коэффициент отражения, резонансная частота, беспроводные системы связиАннотация
В статье предложен подход к ускоренному проектированию микрополосковых патч-антенн на основе методов машинного обучения. Задача синтеза сформулирована как регрессия, где входными признаками являются геометрические параметры антенны и характеристики подложки, а выходными величинами — резонансная частота и и коэффициент отражения . Для обучения сформирован синтетический physics-informed датасет объемом N=1500, охватывающий три диапазона рабочих частот: 2,4; 3,5 и 5 ГГц. Проведено сравнение моделей Random Forest, SVR, ANN и XGBoost. Наилучшие результаты показали ансамблевые методы: XGBoost достиг RMSE=1,663 dB и R2=0,9106 для , а также RMSE=0,0893 GHz и R2=0,9817 для . Полученные результаты подтверждают применимость ML-подхода для предварительного проектирования и интеллектуальной оптимизации параметров патч-антенн при снижении вычислительных затрат.
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