Abstract
In this paper, the effects of data representations and preprocessing on machine learning based S-parameter modeling of high-speed channels are investigated. Using a transformer network as a base model, two S-parameter representations in real/imaginary and magnitude/phase are compared and studied. Considering S-parameter data distributions, various preprocessing techniques including MinMax normalization, standardization, robust scaling, power transformation, and quantile transformation are compared and analyzed to improve accuracy. Moreover, the accuracy results are compared depending on the electrical length of target channels.
Recommended Citation
H. Park et al., "Data Representation and Preprocessing Effects on S-Parameter Modeling of High-Speed Channels using Machine Learning," IEEE International Symposium on Electromagnetic Compatibility, pp. 142 - 147, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/EMCSIPI52291.2025.11170299
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Data preprocess; Data representation; Machine learning; S-parameter
International Standard Serial Number (ISSN)
2158-1118; 1077-4076
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
