Nie, X., Coca, D. orcid.org/0000-0003-2878-2422, Luo, J. et al. (1 more author) (2020) Solving the inverse Frobenius-Perron problem using stationary densities of dynamical systems with input perturbations. Communications in Nonlinear Science and Numerical Simulation, 90. 105302. ISSN 1007-5704
Abstract
Stationary density functions statistically characterize the stabilized behavior of dynamical systems. Instead of temporal sequences of data, stationary densities are observed to determine the unknown transformations, which is called the inverse Frobenius-Perron problem. This paper proposes a new approach to determining the unique map from stationary densities generated by a one-dimensional discrete-time dynamical system driven by an external control input, given the input density functions that are linearly independent. A numerical simulation example is used to validate the effectiveness of the developed approach.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier. This is an author produced version of a paper subsequently published in Communications in Nonlinear Science and Numerical Simulation. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Nonlinear systems; Chaotic maps; Asymptotic stability; Stationary densities; Inverse Frobenius-Perron problem |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Biotechnology and Biological Sciences Research Council BB/M025527/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jun 2020 11:19 |
Last Modified: | 16 May 2021 00:38 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.cnsns.2020.105302 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161682 |