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High-precision antenna modeling using recurrent neural networks with bidirectional LSTM layers and dimensionality reduc-tion
Abstract
This research introduces a novel methodology for high-accuracy modeling of antenna characteristics. It is centered around a recurrent neural network (RNN) optimized through Bayesian Optimization (BO). The RNN architecture includes—among others—a feature input layer, a Long Short-Term Memory (LSTM) layer, a Gated Recurrent Unit (GRU) layer, and a bidirectional LSTM layer. BO is employed to fine-tune the network's hyperparameters, such as the number of units in each layer and the learning rate. The LSTM, GRU, and bidirectional LSTM layers are designed to capture frequency-wise dependencies in antenna responses. A distinctive feature of this approach is the utilization of frequency as a sequential parameter processed by the RNN, which enhances reliability and cost efficiency. These advantages are further bolstered by dimensionality reduction techniques based on rapid global sensitivity analysis. The proposed method has undergone extensive validation across four antennas and has been compared against several benchmark methods, including kernel-based regression models and various neural networks. The results demonstrate that our surrogates achieve outstanding accuracy, significantly surpassing the benchmark, and can construct reliable metamodels even with low-cardinality training datasets. Design applications and experimental validation have also been included.
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Authors (3)
-
Kaustab Sahu
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
no. 76,
pages 4011 - 4025,
ISSN: 0018-926X - Language:
- English
- Publication year:
- 2025
- Bibliographic description:
- Sahu K., Kozieł S., Pietrenko-Dąbrowska A.: High-precision antenna modeling using recurrent neural networks with bidirectional LSTM layers and dimensionality reduc-tion// IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION -Vol. 76,iss. 6 (2025), s.4011-4025
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/tap.2025.3534987
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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