Optimal selection of model order for a class of nonlinear systems using the bootstrap

Author

Zoubir, Abdelhak M. ; Ralston, Jonathon C. ; Iskander, D. Robert

Author_Institution

Signal Processing Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia

Volume

5

fYear

1997

fDate

21-24 Apr 1997

Firstpage

3945

Abstract

Nonlinear system identification involves selecting the order of the given model based on the input-output data. A bootstrap model selection procedure which selects the model by minimising bootstrap estimates of the prediction error is developed. Bootstrap based model selection procedures are attractive because the bootstrap observations generated for the model selection can also be used in subsequent inference procedures. The proposed method is simple and computationally efficient

Keywords

Volterra series; error analysis; identification; nonlinear systems; optimisation; prediction theory; probability; signal processing; Hammerstein series; Volterra series; bootstrap estimates; bootstrap model selection; bootstrap observations; computationally efficient method; inference procedures; input-output data; nonlinear system identification; optimal model order selection; prediction error; probabilities; signal processing; Australia; Costs; Kernel; Nonlinear systems; Parameter estimation; Predictive models; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.604781

Filename

604781