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arXiv · 1009.1670

Convex Optimization In Identification Of Stable Non-Linear State Space Models

Mark M. Tobenkin·Ian R. Manchester·Jennifer Wang·Alexandre Megretski·Russ Tedrake
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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.

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Mark M. Tobenkin, Ian R. Manchester, Jennifer Wang, Alexandre Megretski, Russ Tedrake. 2010-09-09. Convex Optimization In Identification Of Stable Non-Linear State Space Models. https://doi.org/10.1109/cdc.2010.5718114

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