Convex Optimization In Identification Of Stable Non-Linear State Space Models
arXiv:1009.1670 · doi:10.1109/CDC.2010.5718114
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.
9 pages, 2 figure, elaboration of same-title paper in 49th IEEE Conference on Decision and Control