This article focuses on the design of model predictive control (MPC) systems for nonlinear
processes that utilize an ensemble of recurrent neural network (RNN) models to predict …

Abstract Machine learning is receiving more attention in classical engineering fields, and in
particular, recurrent neural networks (RNNs) coupled with ensemble regression tools have …

We present a machine learning-based predictive control scheme that integrates an online
update of the recurrent neural network (RNN) models to capture process nonlinear …

This work explores the design of distributed model predictive control (DMPC) systems for
nonlinear processes using machine learning models to predict nonlinear dynamic behavior …

Recurrent neural networks (RNN) have demonstrated their ability in providing a remarkably
accurate modeling approximation to describe the dynamic evolution of complex, nonlinear …

In this work, physics-based recurrent neural network (RNN) modeling approaches are
proposed for a general class of nonlinear dynamic process systems to improve prediction …

This work focuses on machine learning modeling and predictive control of nonlinear
processes using noisy data. We use long short‐term memory (LSTM) networks with training …

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 …

A neural-network-based technique for developing nonlinear dynamic models from empirical
data for an model predictive control (MPC) algorithm is presented. These models can be …

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 …