ArticleSensors (Basel, Switzerland)2021
LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- Model predictive control of anaerobic digestion processes using a long short-term memory network predictor.Water science and technology : a journal of the International Association on Water Pollution Research · 2025Article
- Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions.Sensors (Basel, Switzerland) · 2025Article
- Comparative Study of Machine Learning and System Identification for Process Systems Engineering Dynamics.Industrial & engineering chemistry research · 2025Article
- Linear matrix genetic programming as a tool for data-driven black-box control-oriented modeling in conditions of limited access to training data.Scientific reports · 2024Article
- Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process.Sensors (Basel, Switzerland) · 2023Article
- Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach.Sensors (Basel, Switzerland) · 2023Article
- Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study.Sensors (Basel, Switzerland) · 2023Article
- Sensor Data Prediction in Missile Flight Tests.Sensors (Basel, Switzerland) · 2022Article
- A review on Natural Language Processing Models for COVID-19 research.Healthcare analytics (New York, N.Y.) · 2022Review
- Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
- The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model.Computational intelligence and neuroscience · 2022Article
- A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags.Sensors (Basel, Switzerland) · 2021Article
- Computationally Efficient Nonlinear Model Predictive Control Using the LSensors (Basel, Switzerland) · 2021Article
Corrections and comments
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This work thoroughly compares the efficiency of Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Unit (GRU) neural networks as models of the dynamical processes used in Model Predictive Control (MPC). Two simulated industrial processes were considered: a polymerisation reactor and a neutralisation (pH) process. First, MPC prediction equations for both types of models were derived. Next, the efficiency of the LSTM and GRU models was compared for a number of model configurations. The influence of the order of dynamics and the number of neurons on the model accuracy was analysed. Finally, the efficiency of the considered models when used in MPC was assessed. The influence of the model structure on different control quality indicators and the calculation time was discussed. It was found that the GRU network, although it had a lower number of parameters than the LSTM one, may be successfully used in MPC without any significant deterioration of control quality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.