Time series prediction with linear and nonlinear adaptive networks

Author

Coughlin, James P. ; Baran, Robert

Author_Institution

Dept. of Math., Towson State Univ., MD, USA

fYear

1991

fDate

18-21 Nov 1991

Firstpage

379

Abstract

Backpropagation networks with a single hidden layer were trained to perform one-step prediction on a variety of scalar time series. The performance of such nets typically equals or exceeds that of the linear adaptive predictor of the same order. Comparisons of the linear and nonlinear predictors were made with periodic, chaotic, and random time series, including broadband ocean acoustic ambient noise

Keywords

adaptive systems; filtering and prediction theory; learning systems; neural nets; time series; backpropagation networks; broadband ocean acoustic ambient noise; linear adaptive networks; neural nets; nonlinear adaptive networks; random time series; scalar time series; time series prediction; Adaptive filters; Adaptive systems; Backpropagation; Chaos; Mathematics; Neural networks; Nonlinear equations; Sea surface; Silver; Springs;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991. 1991 IEEE International Joint Conference on

Print_ISBN

0-7803-0227-3

Type

conf

DOI

10.1109/IJCNN.1991.170431

Filename

170431

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2622958