Learning Complexity Dimensions for a Continuous-Time Control System
Optimization and Control
2007-05-23 v2 Machine Learning
Abstract
This paper takes a computational learning theory approach to a problem of linear systems identification. It is assumed that input signals have only a finite number k of frequency components, and systems to be identified have dimension no greater than n. The main result establishes that the sample complexity needed for identification scales polynomially with n and logarithmically with k.
Cite
@article{arxiv.math/0012163,
title = {Learning Complexity Dimensions for a Continuous-Time Control System},
author = {Pirkko Kuusela and Daniel Ocone and Eduardo D. Sontag},
journal= {arXiv preprint arXiv:math/0012163},
year = {2007}
}
Comments
33 pages