Boumediene Hamzi
Empirical Estimators for Stochastically Forced Nonlinear Systems: Observability, Controllability and the Invariant Measure.
Thu, 04/05/2012 - 15:01 — SomNambulAuthors: Jake Bouvrie, Boumediene Hamzi
Subjects: Optimization and Control
AbstractWe introduce a data-based approach to estimating key quantities which arise
in the study of nonlinear control systems and random nonlinear dynamical
systems. Our approach hinges on the observation that much of the existing
linear theory may be readily extended to nonlinear systems - with a reasonable
expectation of success - once the nonlinear system has been mapped into a high
or infinite dimensional feature space.Model Reduction for Nonlinear Control Systems using Kernel Subspace Methods.
Thu, 08/18/2011 - 15:01 — SomNambulAuthors: Jake Bouvrie, Boumediene Hamzi
Subjects: Optimization and Control
AbstractWe introduce a data-driven order reduction method for nonlinear control
systems, drawing on recent progress in machine learning and statistical
dimensionality reduction. The method rests on the assumption that the nonlinear
system behaves linearly when lifted into a high (or infinite) dimensional
feature space where balanced truncation may be carried out implicitly.Balanced Reduction of Nonlinear Control Systems in Reproducing Kernel Hilbert Space.
Mon, 11/15/2010 - 16:01 — SomNambulAuthors: Jake Bouvrie, Boumediene Hamzi
Subjects: Optimization and Control
AbstractWe introduce a novel data-driven order reduction method for nonlinear control
systems, drawing on recent progress in machine learning and statistical
dimensionality reduction. The method rests on the assumption that the nonlinear
system behaves linearly when lifted into a high (or infinite) dimensional
feature space where balanced truncation may be carried out implicitly.
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