ArticleSensors (Basel, Switzerland)2023
Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- MultivariateSystem Identification of Differential Drive Robot: Comparison Between State-Space and LSTM-Based Models.Sensors (Basel, Switzerland) · 2025Article
- Convergence and stability analysis of recurrent neural networks for rapid structural damage assessment under seismic loads.PloS one · 2025Article
- A Machine Learning Model Based on GRU and LSTM to Predict the Environmental Parameters in a Layer House, Taking COSensors (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
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 specialized data fusion block that relies on fuzzy logic. The second objective of this work is to detail a computationally efficient model predictive control (MPC) algorithm that employs the PIHNN model. The validity of the presented modeling and MPC approaches is demonstrated for a simulated polymerization reactor. It is shown that the PIHNN structure gives very good modeling results, while the MPC controller results in excellent control quality.
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Registered trials
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