White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

System identification from multiple short-time-duration signals

Anderson, S.R., Dean, P., Kadirkamanathan, V., Kaneko, C.R.S. and Porrill, J. (2007) System identification from multiple short-time-duration signals. IEEE Transactions on Biomedical Engineering, 54 (12). pp. 2205-2213. ISSN 0018-9294


Download (677Kb)


System identification problems often arise where the only modeling records available consist of multiple short-time-duration signals. This motivates the development of a modeling approach that is tailored for this situation. An identification algorithm is presented here for parameter estimation based on minimizing the simulated prediction error, across multiple signals. The additional complexity of estimating the initial states corresponding to each signal is removed from the estimation algorithm. A numerical simulation demonstrates that the proposed algorithm performs well in comparison to the often-used least squares method (which leads to biased estimates when identifying systems from measurement noise corrupted signals). The approach is applied to the identification of the passive oculomotor plant; parameters are estimated that describe the dynamics of the plant, which represent the time constants of the visco-elastic elements that characterize the plant connective tissue.

Item Type: Article
Copyright, Publisher and Additional Information: © Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield)
The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 10 Jan 2008 14:03
Last Modified: 20 Jun 2014 15:24
Published Version: http://dx.doi.org/10.1109/TBME.2007.896593
Status: Published
Publisher: IEEE-INST Electrical Electronics Enigeers INC
Refereed: Yes
Identification Number: 10.1109/TBME.2007.896593
URI: http://eprints.whiterose.ac.uk/id/eprint/3555

Actions (repository staff only: login required)