A statistical method for selecting block structures based on estimated Volterra kernels

Author

Dempsey, Erika J. ; Sill, Jeffrey M. ; Westwick, David T.

Author_Institution

Dept. of Electr. & Comput. Eng., Calgary Univ., Alta., Canada

Volume

3

fYear

2003

fDate

17-21 Sept. 2003

Firstpage

2726

Abstract

A method for classifying systems as appropriate block structured models is developed based on estimates of Volterra kernels and their variances. While the traditional methods for classifying systems as block structured models are qualitative, quantitative criteria are proposed. Exact least squares regression is used to estimate Volterra kernels so that the statistics associated with these regressions can be applied to the kernel values and to any linear function of the estimates. Methods for LNL, Hammerstein and Wiener cascades are developed and the results for Wiener system classification are presented in detail.

Keywords

Volterra series; classification; physiological models; regression analysis; Hammerstein cascade; Volterra kernels; Wiener cascade; Wiener system classification; block structures; least squares regression; statistical method; Biological system modeling; Biological systems; Buildings; Fading; Kernel; Least squares approximation; Nonlinear systems; Statistical analysis; Statistics; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE

ISSN

1094-687X

Print_ISBN

0-7803-7789-3

Type

conf

DOI

10.1109/IEMBS.2003.1280480

Filename

1280480