Neural network and time series identification and prediction

Author

Neji, Zouhour ; Beji, F. Mouria

Author_Institution

Lab. d´´Intelligence Artificielle, Ecole Nat. des Sci. de l´´Inf., Tunis, Tunisia

Volume

4

fYear

2000

fDate

2000

Firstpage

461

Abstract

For some classes of nonlinear systems or time series, an operating point dependent NARMA model can be used to present the system. In this paper we attempt to design artificial neural networks that can help in the automatic identification and prediction of such model, for this purpose, we use the Extended Sample Autocorrelation Function (ESACF) as a feature extractor for the network identification and the robust ANN filter for the robust prediction. The network is tested via different noise level in the identification and prediction process to show the accuracy of the connectionist approach and its robust estimation

Keywords

feature extraction; identification; neural nets; time series; Extended Sample Autocorrelation Function; NARMA model; NARMA model identification; artificial neural networks; feature extractor; identification; network identification; nonlinear systems; prediction; robust ANN filter; robust estimation; time series; Artificial neural networks; Autocorrelation; Feature extraction; Filters; Neural networks; Noise level; Noise robustness; Nonlinear systems; Predictive models; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.860814

Filename

860814