Dynamic System Identification from Scarce and Noisy Data Using Symbolic Regression
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- @InProceedings{Cohen:2023:CDC,
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author = "Benjamin Cohen and Burcu Beykal and George Bollas",
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booktitle = "2023 62nd IEEE Conference on Decision and Control
(CDC)",
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title = "Dynamic System Identification from Scarce and Noisy
Data Using Symbolic Regression",
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year = "2023",
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pages = "3670--3675",
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abstract = "A framework for dynamic system model identification
from scarce and noisy data is proposed. This framework
uses symbolic regression via genetic programming with a
gradient-based parameter estimation step to identify a
differential equation model and its parameters from
available system data. The effectiveness of the method
is demonstrated by identifying four synthetic systems:
an ideal plug flow reactor (PFR) with an irreversible
chemical reaction, an ideal continuously stirred tank
reactor (CSTR) with an irreversible chemical reaction,
a system described by Burgers' Equation, and an ideal
PFR with a reversible chemical reaction. The results
show that this framework can identify PDE models of
systems from broadly spaced and noisy data. When the
data was not sufficiently rich, the framework
discovered a surrogate model that described the
observations in equal or fewer terms than the true
system model. Additionally, the method can select
relevant physics terms to describe a system from a list
of candidate arguments, providing valuable models for
use in controls applications.",
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keywords = "genetic algorithms, genetic programming, Parameter
estimation, Chemical reactions, Mathematical models,
Data models, Noise measurement, Inductors",
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DOI = "doi:10.1109/CDC49753.2023.10383906",
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ISSN = "2576-2370",
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month = dec,
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notes = "Also known as \cite{10383906}",
- }
Genetic Programming entries for
Benjamin Cohen
Burcu Beykal
George M Bollas
Citations