Estimation of Difficult-to-Measure Process Variables Using Neural Networks - A comparison of simple MLP and RBF neural network properties (CROSBI ID 496749)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Slišković, Dražen ; Nyarko, Emmanuel Karlo ; Perić, Nedjeljko
engleski
Estimation of Difficult-to-Measure Process Variables Using Neural Networks - A comparison of simple MLP and RBF neural network properties
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 (multi- layer 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.
difficult-to-measure process variable; estimation; soft-sensor; artificial neural networks
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
387-390-x.
2004.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of 12th IEEE Mediterranean Electrotechnical Conference, MELECON 2004
Zagreb:
Podaci o skupu
The 12th IEEE Mediterranean Electrotechnical Conference, MELECON 2004
poster
12.05.2004-15.05.2004
Dubrovnik, Hrvatska