Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions

The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probability density functions that are generated by the stochastic dynamical systems and observed experi...

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Detalles Bibliográficos
Autores principales: Nie, Xiaokai, Luo, Jingjing, Coca, Daniel, Birkin, Mark, Chen, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6018646/
https://www.ncbi.nlm.nih.gov/pubmed/30008519
http://dx.doi.org/10.1007/s00332-018-9455-0
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author Nie, Xiaokai
Luo, Jingjing
Coca, Daniel
Birkin, Mark
Chen, Jing
author_facet Nie, Xiaokai
Luo, Jingjing
Coca, Daniel
Birkin, Mark
Chen, Jing
author_sort Nie, Xiaokai
collection PubMed
description The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probability density functions that are generated by the stochastic dynamical systems and observed experimentally.
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spelling pubmed-60186462018-07-11 Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions Nie, Xiaokai Luo, Jingjing Coca, Daniel Birkin, Mark Chen, Jing J Nonlinear Sci Article The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probability density functions that are generated by the stochastic dynamical systems and observed experimentally. Springer US 2018-03-21 2018 /pmc/articles/PMC6018646/ /pubmed/30008519 http://dx.doi.org/10.1007/s00332-018-9455-0 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Nie, Xiaokai
Luo, Jingjing
Coca, Daniel
Birkin, Mark
Chen, Jing
Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions
title Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions
title_full Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions
title_fullStr Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions
title_full_unstemmed Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions
title_short Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions
title_sort identification of stochastically perturbed autonomous systems from temporal sequences of probability density functions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6018646/
https://www.ncbi.nlm.nih.gov/pubmed/30008519
http://dx.doi.org/10.1007/s00332-018-9455-0
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