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 …

The use of feedforward neural networks for process modeling has proven very successful for
steadystate applications, but suitable applications for dynamic systems are still being …

A neural network architecture incorporating time dependency explicitly, proposed recently,
for modeling nonlinear nonstationary dynamic systems is further developed in this paper …

A method of nonhlinear static and dynamic process modeling via recurrent neural networks
(RNNs) is studied. An RNN model is a set of coupled nonlinear ordinary differential …

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 …

In recent years, multilayer feedforward neural networks have been used for chemical
process identification [9]. One argument used repeatedly against neural network models is …

This paper reviews new techniques to improve neural network model robustness for
nonlinear process modeling and control. The focus is on multiple neural networks. Single …

A new strategy for integrating system identification and predictive control is proposed. A
novel feedforward neural-network architecture is developed to model the system. The …

A new technique for neural‐network‐based modeling of chemical processes is proposed.
Stacked neural networks allow multiple neural networks to be selected and used to model a …

Neural network models trained by dynamic process data alone can lack static representation
capability. In process control applications, it is desirable that process models be able to …