Kernel Methods for the Approximation of Nonlinear Systems

ABS methods for nonlinear systems of algebraic equations

 Abstract    This paper gives a survey of the theory and practice of nonlinear ABS methods including various types of generalizations and computer testing. We also show three applications to special problems, two of which are new.

Model Reduction for Nonlinear Control Systems using Kernel Subspace Methods

We introduce a data-driven model approximation method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction. The method is based on embedding the nonlinear system in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where linear balanced truncation may be carried out implicitly. This leads to a nonlinear red...

Nonlinear Methods of Approximation

Training set approximation for kernel methods

We propose a technique for a training set approximation and its usage in kernel methods. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation are properly selected vectors from the tra...

Approximation Methods for Inverse Problems Governed by Nonlinear Parabolic Systems

We present a rigorous theoretical framework for approximation of nonlinear parabolic systems with delays in the context of inverse least squares problems. Convergence of approximate optimal parameters and that of forward solutions in the context of semidiscrete Galerkin schemes are given. Sample numerical results demonstrating the convergence are given for a model of dioxin uptake and eliminati...