Convex Optimization In Identification Of Stable Nonlinear State Space Models

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.

Biography:

Mark Tobenkin is a PhD student under Russ Tedrake and Alexandre Megretski studying applications of convex optimization and algebraic verification to system identification and feedback stabilization. He received a B.S. in Computer Science and a M. Eng. from the EECS deparment at MIT.