Model structure selection for nonlinear system identification using feedforward neural networks (CROSBI ID 477635)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Petrović, Ivan ; Baotić, Mato ; Perić, Nedjeljko
engleski
Model structure selection for nonlinear system identification using feedforward neural networks
A nonlinear black-box structure for a dynamic system is a model structure that is prepared to describe virtually any nonlinear dynamics. The majority of nonlinear models based on neural networks are of the black-box structure. A nonlinear system can be nonlinear in many different ways, thus the nonlinear black-box model structure must be very flexible. This means that it must have many parameters. A model offering many parameters usually creates problems, and the variance contribution to the error might be high. For a particular identification problem, only a subset of the parameters might be necessary and the main topic in nonlinear system identification is how to select a model structure that describes the system dynamics with the minimum number of parameters. This paper discusses nonlinear input-output models that are suitable for implementation of feedforward neural networks. The proposed model structures were tested and compared using the identification procedure of a pHprocess. The results indicated that it would be worthwhile using the simplest model structure that can satisfactorily represent the investigated process.
neural networks; system modeling
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Podaci o prilogu
53-57-x.
2000.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Vol.1
Podaci o skupu
IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS
predavanje
24.07.2000-27.07.2000
Como, Italija