This paper is concerned with information theoretic metrics for comparing two dynamical systems. Following the recent work of Tryphon Georgiou , we outline a prediction (filtering) based approach to do so. Central to the considerations of this paper is the notion of uncertainty. In particular, we compare systems in terms of additional uncertainty that results for the prediction problem with an incorrect choice of the model. While used variance of the prediction error, we quantify the additional uncertainty in terms of the KullbackLeibler rate. This pseudo-metric is closely related to the classical Bode formula in control theory and we provide detailed comparison to the variance based metric.
- Information theory in control
- model comparison
- nonlinear systems
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering