Machine Learning Approaches for Transformer Modeling
Author:
Affiliation:
1. Instituto de Telecomunicações,Lisbon,Portugal
2. CSIC and Universidad de Sevilla,Instituto de Microelectrónica de Sevilla,Seville,Spain
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9816063/9816175/09816303.pdf?arnumber=9816303
Reference18 articles.
1. A Broadband and Equivalent-Circuit Model for Millimeter-Wave On-Chip M:N Six-Port Transformers and Baluns
2. Radio-frequency inductor synthesis using evolutionary computation and Gaussian-process surrogate modeling
3. An efficient transformer modeling approach for mm-wave circuit design
4. Conversions between S, Z, Y, H, ABCD, and T parameters which are valid for complex source and load impedances
5. Demystifying Surrogate Modeling for Circuits and Systems
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1. Missing Data Imputation Using Transformer Model for Distribution System State Estimation;2023 23rd International Conference on Control, Automation and Systems (ICCAS);2023-10-17
2. Efficient Hierarchical mm-Wave System Synthesis with Embedded Accurate Transformer and Balun Machine Learning Models;Proceedings of the 28th Asia and South Pacific Design Automation Conference;2023-01-16
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