Estimation of difficult-to-measure process variables using neural networks - a comparison of simple MLP and RBF neural network properties

Abstract

In this paper, two different artificial neural networks are tested and compared with regard to their application in the estimation of difficult-to-measure process variables. Two of the most commonly used neural networks, the MLP (multilayer perceptron) and RBF (radial basis function) neural networks, with simple structure and standard training methods are chosen as examples. Neural network training is based on available data from a database of process variables measured over a long time period. The database in this paper is obtained using a simulation model of a real process. Without going deeper into theoretical background, relative properties of these neural networks are given through the results obtained by testing the trained networks and analysis performed on these results.