Practical equivalent electrical circuit identification for electrochemical impedance spectroscopy analysis with gene expression programming
- Author
- Maxime Van Haeverbeke (UGent) , Michiel Stock (UGent) and Bernard De Baets (UGent)
- Organization
- Abstract
- Researchers relying on electrochemical impedance spectroscopy need to decide which equivalent electrical circuit to use to analyze their measurements. Here, we present an identification algorithm based on gene expression programming to support this decision. It is accompanied by some measures to enhance the interpretability of the resulting circuits, such as the removal of redundant components to avoid overly complex circuits. We also provide the option to depart from an initial population of widely applied circuits, allowing for quick identification of known circuits that are capable of modeling the measurement data. As the number of measurements per experiment is typically rather limited in real-life experiments, we examine the number needed to find an adequate circuit topology for two example circuits. Next, the algorithm is tested on impedance simulations for a variety of circuits. Noise robustness is evaluated by subjecting the impedance measurements to increasing amounts of Gaussian noise, demonstrating that the algorithm still works well even for noise levels that are significantly higher than what is typically encountered in practice. Finally, we validate the algorithm by identifying the appropriate circuit for impedance measurements from a biological application.
- Keywords
- Electrical and Electronic Engineering, Instrumentation, Electrochemical impedance spectroscopy (EIS), equivalent electrical circuit, gene expression programmin (GEP), measurement noise, ELECTRODE, CAPACITANCE, EIS
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8722295
- MLA
- Van Haeverbeke, Maxime, et al. “Practical Equivalent Electrical Circuit Identification for Electrochemical Impedance Spectroscopy Analysis with Gene Expression Programming.” IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, vol. 70, 2021, doi:10.1109/tim.2021.3113116.
- APA
- Van Haeverbeke, M., Stock, M., & De Baets, B. (2021). Practical equivalent electrical circuit identification for electrochemical impedance spectroscopy analysis with gene expression programming. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 70. https://doi.org/10.1109/tim.2021.3113116
- Chicago author-date
- Van Haeverbeke, Maxime, Michiel Stock, and Bernard De Baets. 2021. “Practical Equivalent Electrical Circuit Identification for Electrochemical Impedance Spectroscopy Analysis with Gene Expression Programming.” IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70. https://doi.org/10.1109/tim.2021.3113116.
- Chicago author-date (all authors)
- Van Haeverbeke, Maxime, Michiel Stock, and Bernard De Baets. 2021. “Practical Equivalent Electrical Circuit Identification for Electrochemical Impedance Spectroscopy Analysis with Gene Expression Programming.” IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70. doi:10.1109/tim.2021.3113116.
- Vancouver
- 1.Van Haeverbeke M, Stock M, De Baets B. Practical equivalent electrical circuit identification for electrochemical impedance spectroscopy analysis with gene expression programming. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. 2021;70.
- IEEE
- [1]M. Van Haeverbeke, M. Stock, and B. De Baets, “Practical equivalent electrical circuit identification for electrochemical impedance spectroscopy analysis with gene expression programming,” IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, vol. 70, 2021.
@article{8722295,
abstract = {{Researchers relying on electrochemical impedance spectroscopy need to decide which equivalent electrical circuit to use to analyze their measurements. Here, we present an identification algorithm based on gene expression programming to support this decision. It is accompanied by some measures to enhance the interpretability of the resulting circuits, such as the removal of redundant components to avoid overly complex circuits. We also provide the option to depart from an initial population of widely applied circuits, allowing for quick identification of known circuits that are capable of modeling the measurement data. As the number of measurements per experiment is typically rather limited in real-life experiments, we examine the number needed to find an adequate circuit topology for two example circuits. Next, the algorithm is tested on impedance simulations for a variety of circuits. Noise robustness is evaluated by subjecting the impedance measurements to increasing amounts of Gaussian noise, demonstrating that the algorithm still works well even for noise levels that are significantly higher than what is typically encountered in practice. Finally, we validate the algorithm by identifying the appropriate circuit for impedance measurements from a biological application.}},
articleno = {{2514612}},
author = {{Van Haeverbeke, Maxime and Stock, Michiel and De Baets, Bernard}},
issn = {{0018-9456}},
journal = {{IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT}},
keywords = {{Electrical and Electronic Engineering,Instrumentation,Electrochemical impedance spectroscopy (EIS),equivalent electrical circuit,gene expression programmin (GEP),measurement noise,ELECTRODE,CAPACITANCE,EIS}},
language = {{eng}},
pages = {{12}},
title = {{Practical equivalent electrical circuit identification for electrochemical impedance spectroscopy analysis with gene expression programming}},
url = {{http://doi.org/10.1109/tim.2021.3113116}},
volume = {{70}},
year = {{2021}},
}
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