Symbolic Regression for Industrial Applications: An NN-Based Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@InProceedings{Calapristi:2024:MetroXRAINE,

• author = "Marco Calapristi and Luca Patane and Francesca Sapuppo and Riccardo Caponetto and Maria Gabriella Xibilia",
• title = "Symbolic Regression for Industrial Applications: An {NN-Based} Approach",
• booktitle = "2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)",
• year = "2024",
• pages = "618--623",
• month = oct,
• 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",
• 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