Nonlinear modeling of physiological systems with multiple inputs

Author

Mitsis, Georgios D. ; Marmarelis, Vasilis Z.

Author_Institution

Dept. of Biomed. Eng., Univ. of Southern California, Los Angeles, CA, USA

Volume

1

fYear

2002

fDate

2002

Firstpage

21

Abstract

Effective modeling of nonlinear dynamic systems can be achieved by employing Laguerre expansions and feedforward artificial neural networks in the form of the Laguerre-Volterra network (LVN). In this paper an extension of the LVN methodology to multiple-input systems is presented. Results from simulated systems show that this method can yield accurate nonlinear models of multiple-input Volterra systems, even when considerable noise is present.

Keywords

feedforward neural nets; noise; physiological models; Laguerre expansions; Laguerre-Volterra network; estimation accuracy; model compactness; multiple-input systems; nonlinear Volterra systems; physiological systems with multiple inputs; simulated systems; Artificial neural networks; Biomedical engineering; Convergence; Convolution; Kernel; Nonlinear dynamical systems; Nonlinear filters; Nonlinear systems; Parameter estimation; Polynomials;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint

ISSN

1094-687X

Print_ISBN

0-7803-7612-9

Type

conf

DOI

10.1109/IEMBS.2002.1134351

Filename

1134351