Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
A tutorial review of neural network modeling approaches for model predictive control
An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …
presented along with its use in model predictive control (MPC). A tutorial on the construction …
[HTML][HTML] A review and perspective on hybrid modeling methodologies
The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …
from knowledge about the system) and nonparametric models (typically deduced from data) …
Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae
Abstract β-Carotene has a positive impact on human health as a precursor of vitamin A.
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …
On recurrent neural networks for learning-based control: recent results and ideas for future developments
This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks
(RNN) in control design applications. The main families of RNN are considered, namely …
(RNN) in control design applications. The main families of RNN are considered, namely …
Model predictive control of nonlinear processes using transfer learning-based recurrent neural networks
Artificial neural networks (ANNs), one of the deep learning techniques that has sparked a lot
of attention recently for its exceptional modeling capabilities of nonlinear systems, are an …
of attention recently for its exceptional modeling capabilities of nonlinear systems, are an …
Data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results
This paper discusses recent developments in the data-based modeling and control of
nonlinear chemical process systems using sparse identification of nonlinear dynamics …
nonlinear chemical process systems using sparse identification of nonlinear dynamics …
Physics-informed machine learning modeling for predictive control using noisy data
Due to the occurrence of over-fitting at the learning phase, the modeling of chemical
processes via artificial neural networks (ANN) by using corrupted data (ie, noisy data) is an …
processes via artificial neural networks (ANN) by using corrupted data (ie, noisy data) is an …
Modeling and control of a chemical process network using physics-informed transfer learning
This work develops a physics-informed transfer learning framework for modeling and control
of a nonlinear process network with limited training data. Unlike the conventional transfer …
of a nonlinear process network with limited training data. Unlike the conventional transfer …
Physics-informed neural network modeling and predictive control of district heating systems
This article addresses the data-based modeling and optimal control of district heating
systems (DHSs). Physical models of such large-scale networked systems are governed by …
systems (DHSs). Physical models of such large-scale networked systems are governed by …
Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation
In recent years, the integration of mechanistic process models with advanced machine
learning techniques has led to the development of hybrid models, which have shown …
learning techniques has led to the development of hybrid models, which have shown …