Basis Markov Partitions and Transition Matrices for Stochastic Systems

dc.creatorBollt, Erik
dc.creatorGora, Pawel
dc.creatorOstruszka, Andrzej
dc.creatorZyczkowski, Karol
dc.date2006-05-08
dc.date.accessioned2026-07-07T07:14:48Z
dc.date.available2026-07-07T07:14:48Z
dc.descriptionWe analyze dynamical systems subjected to an additive noise and their deterministic limit. In this work, we will introduce a notion by which a stochastic system has something like a Markov partition for deterministic systems. For a chosen class of the noise profiles the Frobenius-Perron operator associated to the noisy system is exactly represented by a stochastic transition matrix of a finite size K. This feature allows us to introduce for these stochastic systems a basis--Markov partition, defined herein, irrespectively of whether the deterministic system possesses a Markov partition or not. We show that in the deterministic limit, corresponding to K --> infinity, the sequence of invariant measures of the noisy systems tends, in the weak sense, to the invariant measure of the deterministic system. Thus by introducing a small additive noise one may approximate transition matrices and invariant measures of deterministic dynamical systems.
dc.description21 pages in latex including 3 figures
dc.identifierhttps://arxiv.org/abs/nlin/0605017
dc.identifierhttp://arxiv.org/abs/nlin/0605017
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/113028
dc.subjectChaotic Dynamics
dc.titleBasis Markov Partitions and Transition Matrices for Stochastic Systems
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