Practical Equivalent Electrical Circuit Identification for Electrochemical Impedance Spectroscopy Analysis With Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{journals/tim/HaeverbekeSB21,

• author = "Maxime {Van Haeverbeke} and Michiel Stock and Bernard {De Baets}",
• title = "Practical Equivalent Electrical Circuit Identification for Electrochemical Impedance Spectroscopy Analysis With Gene Expression Programming",
• journal = "IEEE Transactions on Instrumentation and Measurement",
• year = "2021",
• volume = "70",
• keywords = "genetic algorithms, genetic programming, gene expression programming, GEP, electrochemical impedance spectroscopy (eis), equivalent electrical circuit, measurement noise",
• ISSN = "0018-9456",
• bibdate = "2021-11-03",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tim/tim70.html#HaeverbekeSB21",
• URL = "https://doi.org/10.1109/TIM.2021.3113116",
• DOI = "doi:10.1109/TIM.2021.3113116",
• size = "12 pages",
• abstract = "Researchers relying on electrochemical impedance spectroscopy need to decide which equivalent electrical circuit to use to analyse 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 modelling 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.",

}

Genetic Programming entries for Maxime Van Haeverbeke Michiel Stock Bernard De Baets

Citations