Convex optimization in identification of stable non-linear state space models

Author(s)
Tobenkin, Mark M.; Manchester, Ian R.; Wang, Jennifer; Megretski, Alexandre; Tedrake, Russell Louis
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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.
Date issued
2010-12
URI
http://hdl.handle.net/1721.1/72943
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
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
ISBN
978-1-4244-7745-6
ISSN
0743-1546
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