Symbolic Regression for Industrial Applications: An NN-Based Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Calapristi:2024:MetroXRAINE,
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author = "Marco Calapristi and Luca Patane and
Francesca Sapuppo and Riccardo Caponetto and Maria Gabriella Xibilia",
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title = "Symbolic Regression for Industrial Applications: An
{NN-Based} Approach",
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booktitle = "2024 IEEE International Conference on Metrology for
eXtended Reality, Artificial Intelligence and Neural
Engineering (MetroXRAINE)",
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year = "2024",
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pages = "618--623",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Deep
learning, Training, Recurrent neural networks,
Accuracy, System dynamics, Soft sensors, Artificial
neural networks, Mathematical models, Encoding, system
identification, dynamical nonlinear systems, neural
networks, ANN, model interpretability",
-
DOI = "
doi:10.1109/MetroXRAINE62247.2024.10797142",
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abstract = "Symbolic Regression (SR) is a machine learning
approach developed for the automated identification of
mathematical equations that accurately capture the
relationships between input and output features within
the experimental dataset. This method is capable of
creating interpretable models while incorporating
existing knowledge into the system. This paper
addresses a problem in the development of interpretable
Soft Sensors (SS) for industrial applications using SR.
The challenge arises from the need to increase the
dimensionality of the problem in order to capture the
system dynamics, which often leads to a significant
degradation in SR performance. Existing literature has
highlighted this problem and proposed some solutions,
such as employing Recurrent Neural Networks (RNN)
instead of Genetic Programming (GP) in the SR procedure
or applying Deep Learning (DL) techniques to reduce the
input space. In this paper, we present a novel approach
to develop interpretable SSs for industrial processes
that involve the use of DL to encode the system
dynamics. This effectively reduces the input space and
supports the SR process without compromising the
interpretability of the final solution.",
-
notes = "Also known as \cite{10797142}",
- }
Genetic Programming entries for
Marco Calapristi
Luca Patane
Francesca Sapuppo
Riccardo Caponetto
Maria Gabriella Xibilia
Citations