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CORR
2000
Springer

Learning Complexity Dimensions for a Continuous-Time Control System

10 years 1 months ago
Learning Complexity Dimensions for a Continuous-Time Control System
This paper takes a computational learning theory approach to a problem of linear systems identification. It is assumed that inputs are generated randomly from a known class consisting of linear combinations of k sinusoidals. The output of the system is classified at some single instant of time. The main result establishes that the number of samples needed for identification with small error and high probability, independently from the distribution of inputs, scales polynomially with n, the system dimension, and logarithmically with k. Key words. linear systems identification, learning theory, VC dimension AMS subject classifications. 68Q32, 68Q17, 93B30, 93C05
Pirkko Kuusela, Daniel Ocone, Eduardo D. Sontag
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2000
Where CORR
Authors Pirkko Kuusela, Daniel Ocone, Eduardo D. Sontag
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