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