@inproceedings{b6616fb73cc64a949f350670a2ea1244,
title = "Identification of Discrete Non-Linear Dynamics of a Radio-Frequency Power Amplifier Circuit using Symbolic Regression.",
abstract = "The identification of non-linearities or undesirable dynamic behavior of electrical components is a common problem. Previous modeling forms are largely based on extensive physical knowledge at the semiconductor level, which has produced reliable solutions over the past decades. This however implies the measurement of physical prototypes in laboratories, which can be costly. It is therefore desirable to have reliable software models of the prototypes available to outsource this procedure to simulators. This paper presents a number of solutions from the field of empirical modeling including symbolic regression, which allow to parameterize such models from measured values. As an example we are utilizing time-domain data from a real radio-frequency power amplifier circuit. We compare a Hammerstein-Wiener model with two methods for symbolic regression, and find that the Hammerstein-Wiener model produces the best predictions but has many non-zero coefficients. Both symbolic regression methods produce short linear models with slightly higher prediction error than the HW model.",
keywords = "Genetic Programming, Non-Linear System Identification, Power Amplifier, Sparse Identification, Symbolic Regression",
author = "Martin Steiger and Brachtendorf, {Hans Georg} and Gabriel Kronberger",
note = "Funding Information: ACKNOWLEDGMENT G.K. acknowledges support by the Christian Doppler Research Association and the Austrian Federal Ministry of Digital and Economic Affairs within the Josef Ressel Center for Symbolic Regression. Funding Information: This project AMOR ATCZ203 has been co-financed by the European Union using financial means of the European Regional Development Fund (INTERREG) for sustainable cross boarder cooperation. Further information on INTERREG Austria-Czech Republic is availableat https://www.at-cz.eu/at. Publisher Copyright: {\textcopyright} 2022 IEEE.",
year = "2022",
doi = "10.1109/SYNASC57785.2022.00054",
language = "English",
isbn = "978-1-6654-6546-5",
series = "Proceedings - 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2022",
publisher = "IEEE",
pages = "297--303",
editor = "Bruno Buchberger and Mircea Marin and Viorel Negru and Daniela Zaharie",
booktitle = "Proceedings - 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2022",
}