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 …

The authors have developed a reliable long-range predictor, comprising two neural
networks with external feedback in series, and investigated its applicability for model …

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 …

This paper discusses an industrial application of a multivariable nonlinear
feedforward/feedback model predictive control where the model is given by a dynamic …

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 …

In the chemical process industry although most systems possess non-linear characteristics,
control algorithms are synthesised based upon linear approximations. In some process …

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 …

The use of artificial neural networks in model based control, both as process models and as
controllers, is investigated: two nonlinear model based control strategies, internal model …

Neural-network techniques are investigated in an application to the identification and
subsequent on-line control of a process exhibiting non-linearities and typical disturbances …

A neural model predictive control strategy combining a neural network for plant identification
and a nonlinear programming algorithm for solving nonlinear control problems is proposed …