ResEntSG: restoration entropy estimation for dynamical systems via Riemannian metric optimization

Kawan, Christoph, Hafstein, Sigurdur Freyr and Giesl, Peter (2021) ResEntSG: restoration entropy estimation for dynamical systems via Riemannian metric optimization. SoftwareX, 15. a100743 1-5. ISSN 2352-7110

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Abstract

In the remote state estimation problem, an observer reconstructs the state of a dynamical system at a remote location, where no direct sensor measurements are available. The estimator only has access to information sent through a digital channel. The notion of restoration entropy provides a way to determine the smallest channel capacity above which an observer can be designed that observes the system without a degradation of the initial estimation error. In general, restoration entropy is hard to compute. We present a class library in C++, that estimates the restoration entropy of a given system by computing an adapted metric for the system. The library is simple to use and implements a version of the subgradient algorithm for geodesically convex functions to compute an optimal metric in a class of conformal metrics. Included in the software are three example systems to demonstrate the use and efficacy of the library.

Item Type: Article
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 16 Jul 2021 06:42
Last Modified: 16 Jul 2021 07:01
URI: http://sro.sussex.ac.uk/id/eprint/100544

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