APA
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Yusuf, M., Dasgupta, A., & Roy, S. (2024). Machine Learning Based Inverse Model for Identifying Transmission Line Network Parameters from Tabulated Frequency-Domain Data. In Electrical Design of Advanced Packaging and Systems Symposium (pp. 1–3). https://doi.org/10.1109/EDAPS64431.2024.10988480
Chicago/Turabian
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Yusuf, Mohd., Avirup Dasgupta, and Sourajeet Roy. “Machine Learning Based Inverse Model for Identifying Transmission Line Network Parameters from Tabulated Frequency-Domain Data.” In Electrical Design of Advanced Packaging and Systems Symposium, 1–3, 2024.
MLA
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Yusuf, Mohd., et al. “Machine Learning Based Inverse Model for Identifying Transmission Line Network Parameters from Tabulated Frequency-Domain Data.” Electrical Design of Advanced Packaging and Systems Symposium, 2024, pp. 1–3, doi:10.1109/EDAPS64431.2024.10988480.
BibTeX Click to copy
@inproceedings{yusuf2024a,
title = {Machine Learning Based Inverse Model for Identifying Transmission Line Network Parameters from Tabulated Frequency-Domain Data},
year = {2024},
journal = {Electrical Design of Advanced Packaging and Systems Symposium},
pages = {1-3},
doi = {10.1109/EDAPS64431.2024.10988480},
author = {Yusuf, Mohd. and Dasgupta, Avirup and Roy, Sourajeet},
booktitle = {2024 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS)}
}
In this paper, a neural network based inverse model is presented that can predict the values of the geometrical, physical, and material parameters of a given transmission line network from its tabulated frequency-domain responses. The proposed inverse model consists of two parts – a forward neural network that emulates the frequency-domain responses of the transmission line network under test as analytic functions of the geometrical, physical, and material parameters and a particle swarm optimizer (PSO) that minimizes an appropriate loss function to derive the requisite values of the network parameters.