Artificial neural networks (ANNs) have been used for the prediction of the energy
consumption of a passive solar building. The building structure consists of one room with an …

Reliable and timely fault detection and isolation are necessary tasks to guarantee
continuous performance in complex industrial systems, avoiding failure propagation in the …

This paper proposes a new approach for constrained multivariable predictive control based
on the use of a recurrent neural network as a non-linear prediction model of the plant under …

An experimental solar steam generator, consisting of a parabolic trough collector, a high-
pressure steam circuit, and a suitable flash vessel has been constructed and tested in order …

The scaling up of a pilot plant fluid catalytic cracking (FCC) model to an industrial unit with
use of artificial neural networks is presented in this paper. FCC is one of the most important …

An artificial neural network (ANN) model for determining the steady-state behaviour of an
industrial Fluid Catalytic Cracking (FCC) unit is presented in this paper. Industrial data from …

The state-space neural network paradigm is a neural model suitable for various applications
in the field of control engineering. In this paper, it is shown how this neural model can be …

Neural networks (NNs) belong to 'black box'models and therefore 'suffer'from interpretation
difficulties. Four recent methods inferring variable influence in NNs are compared in this …

In this paper, a neural network based predictive control structure is proposed and applied to
treat the problem of diabetes management. The control approach is to predict future plant …