Comparison of the traditional and the neural networks approaches in a stochastic nonlinear system identification

Author

Chong, Kil To ; Parlos, Alexander G.

Author_Institution

Chon Buk Nat. Univ., South Korea

Volume

2

fYear

1997

fDate

4-6 Jun 1997

Firstpage

1074

Abstract

The comparison between the neural networks and traditional approaches as a nonlinear system identification method is investigated in the aspects of the models´ performance. Two neural networks models which are of the state space and the input/output model structures are considered as neural networks models. In the traditional methods an autoregressive exogeneous input model and a nonlinear autoregressive exogeneous input model are considered

Keywords

autoregressive processes; identification; neural nets; nonlinear systems; state-space methods; stochastic systems; ARX model; I/O model structures; NARX model; input/output model structures; model performance; neural networks; nonlinear autoregressive exogeneous input model; state space model structures; stochastic nonlinear system identification; Artificial neural networks; Biological system modeling; Crosstalk; Electronic mail; Intelligent networks; Multi-layer neural network; Neural networks; Nonlinear systems; Stochastic systems; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.609696

Filename

609696