224309
20180317094951.0
ARTICLE
Identification of NARX models using regularization networks: A consistency result
1998
1998
Journal Articles
Anchorage, Alaska, US.
Generalization networks are nonparametric estimators obtained from the application of Tychonov regularization or Bayes estimation to the hypersurface reconstruction problem. Under symmetry assumptions they are a particular type of radial basis function neural networks. In the paper it is shown that such networks guarantee consistent identification of a very general (infinite dimensional) class of NARX models. The proofs are based on the theory of reproducing kernel Hilbert spaces and the notion of frequency of time probability, by means of which it is not necessary to assume that the input is sampled from a stochastic process.
De Nicolao, G.
Ferrari-Trecate, G.
2407-2412
Proc. IEEE World Congr. on Computat. Intelligence
oai:infoscience.tind.io:224309
article
STI
SCI-STI-GFT
252594
U13313
EPFL-ARTICLE-224309
DNFT98b/GFT
OTHER
PUBLISHED
REVIEWED
ARTICLE