Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)

High-Frequency Electronic Modeling Using Neural Networks

Authors
Qijun Zhang
Corresponding Author
Qijun Zhang
Available Online March 2018.
DOI
https://doi.org/10.2991/acaai-18.2018.31How to use a DOI?
Keywords
modeling; neural networks; high-frequency electronics; microwave
Abstract

We present an overview of neural network approaches for efficient modeling, simulation and optimization of high-frequency electronic and microwave circuits. Neural networks trained from electronic/microwave data are subsequently used for circuit simulation and optimization. A brief introduction of the fundamentals of neural network structures and neural model development issues including data generation, and neural network training are summarized. Applications of neural networks for modeling, simulation and optimization of transmission line networks for interconnect analysis on printed circuit board design are illustrated.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
Series
Advances in Intelligent Systems Research
Publication Date
March 2018
ISBN
978-94-6252-483-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/acaai-18.2018.31How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Qijun Zhang
PY  - 2018/03
DA  - 2018/03
TI  - High-Frequency Electronic Modeling Using Neural Networks
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 134
EP  - 136
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.31
DO  - https://doi.org/10.2991/acaai-18.2018.31
ID  - Zhang2018/03
ER  -