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 …

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 …

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 …

The design of neural networks (NNs) is presented for treating large, linear model predictive
control (MPC) applications that are out of reach with available quadratic programming (QP) …

Implementation of a neural network model-based predictive control scheme to a laboratory-
scaled multivariable chemical reactor is described in this paper. Three variables are …

Neural networks hold great promise for application in the general area of process control.
This paper focuses on using a back propagation network in an optimization based model …

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

As a result of good modeling capabilities, neural networks have been used extensively for a
number of chemical engineering applications such as sensor data analysis, fault detection …

Recently, neural network dynamic modeling has drawn a great deal of attentionnot only from
academia but also from industry. It has been shown that neural networks can learn to mimic …