Practical comparison of neural networks and conventional identification methodologies
1997
This paper addresses practical comparison between the conventional identification methodology and identification based on computational intelligence (CI). For this purpose, Auto-Regressive Moving Average with eXogenous inputs (ARMAX), Non-Linear ARMAX (NARMAX) and identifications based on Artificial Neural Networks (ANN) are applied to the modelling of a pilot-scale parallel-tube heat exchanger. First and second-order non-linear optimisation methods are used to train the neural networks. Results of the identification methods are presented and compared. It is shown that the use of second-order non-linear optimisation method for training neural networks yields a significant improvement in the convergence rate.
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