Convex optimization in identification of stable non-linear state space models
Name
Megretski_Convex optimization.pdf
Size
291.87 KB
Format
Adobe PDF
Checksum (MD5)
8f7dfa8220e9fb1367b6ea9b3ccd0cd7
Author(s)
Tobenkin, Mark M.
Manchester, Ian R.
Wang, Jennifer
Megretski, Alexandre
Tedrake, Russell Louis
Date Issued
December 2010
Journal
Proceedings of the 49th IEEE Conference on Decision and Control (CDC), 2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Tobenkin, Mark M. et al. “Convex Optimization in Identification of Stable Non-linear State Space Models.” Proceedings of the 49th IEEE Conference on Decision and Control (CDC), 2010. 7232–7237. © Copyright 2010 IEEE
Version
Final published version
Abstract
A new framework for nonlinear system identification is presented in terms of optimal fitting of stable nonlinear state space equations to input/output/state data, with a performance objective defined as a measure of robustness of the simulation error with respect to equation errors. Basic definitions and analytical results are presented. The utility of the method is illustrated on a simple simulation example as well as experimental recordings from a live neuron.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC.2010.5718114
