A LEARNING APPROACH TO IDENTIFICATION OF NONLINEAR PHYSIOLOGICAL SYSTEMS USING WIENER MODELS

Xingjian Jing, Natalia Angarita-Jaimes, David Simpson, Robert Allen, Philip Newland

2011

Abstract

The Wiener model is a natural description of many physiological systems. Although there have been a number of algorithms proposed for the identification of Wiener models, most of the existing approaches were developed under some restrictive assumptions of the system such as a white noise input, part or full invertibility of the nonlinearity, or known nonlinearity. In this study a new recursive algorithm based on Lyapunov stability theory is presented for the identification of Wiener systems with unknown and noninvertible nonlinearity and noisy data. The new algorithm can guarantee global convergence of the estimation error to a small region around zero and is as easy to implement as the well-known back propagation algorithm. Theoretical analysis and example studies show the effectiveness and advantages of the proposed method compared with the earlier approaches.

Paper Citation

in Harvard Style

Jing X., Angarita-Jaimes N., Simpson D., Allen R. and Newland P. (2011). A LEARNING APPROACH TO IDENTIFICATION OF NONLINEAR PHYSIOLOGICAL SYSTEMS USING WIENER MODELS . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 472-476. DOI: 10.5220/0003163704720476

in Bibtex Style

@conference{biosignals11,
author={Xingjian Jing and Natalia Angarita-Jaimes and David Simpson and Robert Allen and Philip Newland},
title={A LEARNING APPROACH TO IDENTIFICATION OF NONLINEAR PHYSIOLOGICAL SYSTEMS USING WIENER MODELS},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={472-476},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003163704720476},
isbn={978-989-8425-35-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - A LEARNING APPROACH TO IDENTIFICATION OF NONLINEAR PHYSIOLOGICAL SYSTEMS USING WIENER MODELS
SN - 978-989-8425-35-5
AU - Jing X.
AU - Angarita-Jaimes N.
AU - Simpson D.
AU - Allen R.
AU - Newland P.
PY - 2011
SP - 472
EP - 476
DO - 10.5220/0003163704720476