Identification of NARX models using regularization networks: a consistency result

Author

De Nicolao, G. ; Trecate, G. Ferrari

Author_Institution

Dipt. di Inf. e Sistemistica, Pavia Univ., Italy

Volume

3

fYear

1998

fDate

4-9 May 1998

Firstpage

2407

Abstract

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

Keywords

Bayes methods; autoregressive processes; feedforward neural nets; generalisation (artificial intelligence); identification; Bayes estimation; NARX models; Tychonov regularization; generalization networks; hypersurface reconstruction problem; identification; kernel Hilbert spaces; nonparametric estimators; radial basis function neural networks; regularization networks; symmetry; time probability frequency; Computational efficiency; Frequency; Hilbert space; Informatics; Kernel; Nonlinear systems; Polynomials; Radial basis function networks; Sampling methods; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.687239

Filename

687239