Naslov
Regularization and validation of neural network models of nonlinear systems

Autori
Petrović, Ivan ; Baotić, Mato ; Perić, Nedjeljko

Izvornik
Elektrotechnik und Informationstechnik (e&i) (0932-383X) 117 (2000), 1; 24-31

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
nonlinear systems; neural networks; regularization techniques; model validation

Sažetak
A characteristic feature of the neural network models is the large number of parameters. A model offering many parameters usually gives rise to problems, and the variance contribution to the modeling error might be very high. Therefore, it is crucial to find the model with the optimal number of parameters. In this paper two techniques of selection of the optimal number of model parameters are described and compared: explicit and implicit regularization techniques. Model validation forms the final stage of an identification procedure with the aim of assessing objectively whether the identified model agrees sufficiently well with the observed data. In this paper the reliability of the correlation-based validation tests and the c2-test is analyzed.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA