Extrapolative models of dynamics systems: neural networks vs. Kalman filter

Author

Mu´awin, A. ; Chowdhury, Fahmida N.

Author_Institution

Dept. of Electr. Eng., Michigan Technol. Univ., Houghton, MI, USA

fYear

1997

fDate

9-11 Mar 1997

Firstpage

315

Lastpage

319

Abstract

The authors present a case study for comparing two modelling techniques for nonlinear systems: neural networks and ARMA (autoregressive moving average). The ARMA model is generated on-line by a Kalman filter, and the coefficients are allowed to be slowly time-varying. The neural network has one hidden layer with sigmoid neurons, and a time-delay structure. Simulations results are presented

Keywords

Kalman filters; autoregressive moving average processes; delays; extrapolation; industrial plants; modelling; neural nets; nonlinear systems; simulation; ARMA; Kalman filter; autoregressive moving average; dynamics systems; extrapolative models; hidden layer; modelling techniques; neural networks; nonlinear systems; sigmoid neurons; simulation; slowly time-varying coefficients; time-delay structure; Autoregressive processes; Delay; Extrapolation; Neural networks; Neurons; Nonlinear equations; Nonlinear systems; Predictive models; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1997., Proceedings of the Twenty-Ninth Southeastern Symposium on

Conference_Location

Cookeville, TN

ISSN

0094-2898

Print_ISBN

0-8186-7873-9

Type

conf

DOI

10.1109/SSST.1997.581648

Filename

581648

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3424327