Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach

Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach

This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description and data-driven neural sub-models using a special...

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Bibliographic Details
Main Authors: Krzysztof Zarzycki, Maciej Ławryńczuk
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Sensors
Subjects:
dynamical systems
LSTM neural networks
physics-informed neural networks
model predictive control
Online Access:https://www.mdpi.com/1424-8220/23/21/8898

Internet

https://www.mdpi.com/1424-8220/23/21/8898

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