2026-11-06
Lower Band-Edge Frequency Prediction for UWB Antennas via Knowledge-Based Artificial Neural Network
By
Progress In Electromagnetics Research M, Vol. 141, 53-64, 2026
Abstract
To improve prediction accuracy for lower band-edge frequencies of ultra-wideband (UWB) printed monopole antennas, we propose a knowledge-based artificial neural network (KBANN) method. This approach uses antenna geometric parameters as inputs and introduces the lower band-edge frequency calculated from empirical formulas as prior knowledge into the neural network, integrating electromagnetic knowledge with machine learning. We construct a simulation dataset by performing electromagnetic simulations on antennas with various shapes and feed locations for model training and evaluation. Experimental comparisons show that the proposed knowledge-based artificial neural network achieves higher prediction accuracy and faster convergence than the empirical formula and data-driven models without prior knowledge, while maintaining lower prediction errors under limited-data conditions. Shapley additive explanation (SHAP) analysis further shows that prior knowledge contributes to the model predictions with the dominant antenna geometric parameters. The proposed method provides an efficient approach for lower band-edge frequency prediction of ultra-wideband antennas.
Citation
Zekun Zhang, Zihan Xu, Shuling Yin, Xia Cao, and Bing Yang, "Lower Band-Edge Frequency Prediction for UWB Antennas via Knowledge-Based Artificial Neural Network," Progress In Electromagnetics Research M, Vol. 141, 53-64, 2026.
DOI:10.2528/PIERM26090901
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