Abstract:
We present an information-theoretically motivated method for dimensionality reduction and realization of linear dynamical systems. Given a linear system with Gaussian inp...View moreMetadata
Abstract:
We present an information-theoretically motivated method for dimensionality reduction and realization of linear dynamical systems. Given a linear system with Gaussian inputs, we apply the Information Bottleneck principle to construct a continuous range of reduced-order systems, each satisfying an optimal trade-off between the compression rate of past inputs to, and the prediction accuracy of future outputs from the original system. We apply a certain variant of the Ho-Kalman algorithm to obtain realizations of the reduced-order systems and show that they lie near the optimal Information Curve. We explore the behavior of a set of systems realized using the method and discuss an extension of the method to closed-loop linear systems.
Published in: 2015 54th IEEE Conference on Decision and Control (CDC)
Date of Conference: 15-18 December 2015
Date Added to IEEE Xplore: 11 February 2016
ISBN Information: