A new approach to identification of input-driven dynamical systems from probability densities

A new approach to identification of input-driven dynamical systems from probability densities

Evolution of probability densities generated by many practical dynamical systems can be more conveniently observed than individual point trajectories. This paper introduces a new method to reconstruct the unknown transformation of a 1D discrete-time dynamical system that is driven by an external con...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Inverse problems 2018-08, Vol.34 (8), p.85004
Hauptverfasser: Nie, Xiaokai, Birkin, Mark, Luo, Jingjing
Format: Artikel
Sprache:eng
Schlagworte:
asymptotic stability
inverse problem
nonlinear dynamical systems
probability density functions
semi-Markov map
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 8
container_start_page 85004
container_title Inverse problems
container_volume 34
creator Nie, Xiaokai
Birkin, Mark
Luo, Jingjing
description Evolution of probability densities generated by many practical dynamical systems can be more conveniently observed than individual point trajectories. This paper introduces a new method to reconstruct the unknown transformation of a 1D discrete-time dynamical system that is driven by an external control input with a given probability density function, using multiple sequences of converging probability densities generated by the perturbed underlying system. Regardless of different initial conditions the generated densities are demonstrated possessing strong convergence to a unique invariant density. Numerical simulation results validate the applicability of the developed algorithm as well as the performance in the presence of stochastic noise.
doi_str_mv 10.1088/1361-6420/aac533
format Article
fullrecord <record><control><sourceid>iop_cross</sourceid><recordid>TN_cdi_iop_journals_10_1088_1361_6420_aac533</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>ipaac533</sourcerecordid><originalsourceid>FETCH-LOGICAL-c280t-ff795250be66a9f073b2cd20e8ff69056bcb27addac7935106009eaf38164de63</originalsourceid><addsrcrecordid>eNp1kE1LxDAQhoMoWFfvHvMDrDtJtml7XBa_YMGLHiWk-cAsbVKSrNJ_b5eKN08DM-_zMjwI3RK4J9A0a8I4KfmGwlpKVTF2hoq_1TkqgHJeVpyQS3SV0gGAkIbUBfrYYm--sRzHGKT6xDlgp43PzjolswseB4udH4-51NF9GY_15OUwH3ucppTNkLCNYcAz38nO9S5PeC5ILjuTrtGFlX0yN79zhd4fH952z-X-9ellt92XijaQS2vrtqIVdIZz2VqoWUeVpmAaa3kLFe9UR2uptVR1yyoCHKA10rKG8I02nK0QLL0qhpSisWKMbpBxEgTESY84uRAnF2LRMyN3C-LCKA7hGP384P_xH_u7aEE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A new approach to identification of input-driven dynamical systems from probability densities</title><source>Institute of Physics Journals</source><creator>Nie, Xiaokai ; Birkin, Mark ; Luo, Jingjing</creator><creatorcontrib>Nie, Xiaokai ; Birkin, Mark ; Luo, Jingjing</creatorcontrib><description>Evolution of probability densities generated by many practical dynamical systems can be more conveniently observed than individual point trajectories. This paper introduces a new method to reconstruct the unknown transformation of a 1D discrete-time dynamical system that is driven by an external control input with a given probability density function, using multiple sequences of converging probability densities generated by the perturbed underlying system. Regardless of different initial conditions the generated densities are demonstrated possessing strong convergence to a unique invariant density. Numerical simulation results validate the applicability of the developed algorithm as well as the performance in the presence of stochastic noise.</description><identifier>ISSN: 0266-5611</identifier><identifier>EISSN: 1361-6420</identifier><identifier>DOI: 10.1088/1361-6420/aac533</identifier><identifier>CODEN: INPEEY</identifier><language>eng</language><publisher>IOP Publishing</publisher><subject>asymptotic stability ; inverse problem ; nonlinear dynamical systems ; probability density functions ; semi-Markov map</subject><ispartof>Inverse problems, 2018-08, Vol.34 (8), p.85004</ispartof><rights>2018 IOP Publishing Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c280t-ff795250be66a9f073b2cd20e8ff69056bcb27addac7935106009eaf38164de63</citedby><cites>FETCH-LOGICAL-c280t-ff795250be66a9f073b2cd20e8ff69056bcb27addac7935106009eaf38164de63</cites><orcidid>0000-0002-2357-2947</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1361-6420/aac533/pdf$$EPDF$$P50$$Giop$$H</linktopdf><link.rule.ids>314,780,784,27924,27925,53846,53893</link.rule.ids></links><search><creatorcontrib>Nie, Xiaokai</creatorcontrib><creatorcontrib>Birkin, Mark</creatorcontrib><creatorcontrib>Luo, Jingjing</creatorcontrib><title>A new approach to identification of input-driven dynamical systems from probability densities</title><title>Inverse problems</title><addtitle>IP</addtitle><addtitle>Inverse Problems</addtitle><description>Evolution of probability densities generated by many practical dynamical systems can be more conveniently observed than individual point trajectories. This paper introduces a new method to reconstruct the unknown transformation of a 1D discrete-time dynamical system that is driven by an external control input with a given probability density function, using multiple sequences of converging probability densities generated by the perturbed underlying system. Regardless of different initial conditions the generated densities are demonstrated possessing strong convergence to a unique invariant density. Numerical simulation results validate the applicability of the developed algorithm as well as the performance in the presence of stochastic noise.</description><subject>asymptotic stability</subject><subject>inverse problem</subject><subject>nonlinear dynamical systems</subject><subject>probability density functions</subject><subject>semi-Markov map</subject><issn>0266-5611</issn><issn>1361-6420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kE1LxDAQhoMoWFfvHvMDrDtJtml7XBa_YMGLHiWk-cAsbVKSrNJ_b5eKN08DM-_zMjwI3RK4J9A0a8I4KfmGwlpKVTF2hoq_1TkqgHJeVpyQS3SV0gGAkIbUBfrYYm--sRzHGKT6xDlgp43PzjolswseB4udH4-51NF9GY_15OUwH3ucppTNkLCNYcAz38nO9S5PeC5ILjuTrtGFlX0yN79zhd4fH952z-X-9ellt92XijaQS2vrtqIVdIZz2VqoWUeVpmAaa3kLFe9UR2uptVR1yyoCHKA10rKG8I02nK0QLL0qhpSisWKMbpBxEgTESY84uRAnF2LRMyN3C-LCKA7hGP384P_xH_u7aEE</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Nie, Xiaokai</creator><creator>Birkin, Mark</creator><creator>Luo, Jingjing</creator><general>IOP Publishing</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-2357-2947</orcidid></search><sort><creationdate>20180801</creationdate><title>A new approach to identification of input-driven dynamical systems from probability densities</title><author>Nie, Xiaokai ; Birkin, Mark ; Luo, Jingjing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c280t-ff795250be66a9f073b2cd20e8ff69056bcb27addac7935106009eaf38164de63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>asymptotic stability</topic><topic>inverse problem</topic><topic>nonlinear dynamical systems</topic><topic>probability density functions</topic><topic>semi-Markov map</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nie, Xiaokai</creatorcontrib><creatorcontrib>Birkin, Mark</creatorcontrib><creatorcontrib>Luo, Jingjing</creatorcontrib><collection>CrossRef</collection><jtitle>Inverse problems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nie, Xiaokai</au><au>Birkin, Mark</au><au>Luo, Jingjing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new approach to identification of input-driven dynamical systems from probability densities</atitle><jtitle>Inverse problems</jtitle><stitle>IP</stitle><addtitle>Inverse Problems</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>34</volume><issue>8</issue><spage>85004</spage><pages>85004-</pages><issn>0266-5611</issn><eissn>1361-6420</eissn><coden>INPEEY</coden><abstract>Evolution of probability densities generated by many practical dynamical systems can be more conveniently observed than individual point trajectories. This paper introduces a new method to reconstruct the unknown transformation of a 1D discrete-time dynamical system that is driven by an external control input with a given probability density function, using multiple sequences of converging probability densities generated by the perturbed underlying system. Regardless of different initial conditions the generated densities are demonstrated possessing strong convergence to a unique invariant density. Numerical simulation results validate the applicability of the developed algorithm as well as the performance in the presence of stochastic noise.</abstract><pub>IOP Publishing</pub><doi>10.1088/1361-6420/aac533</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-2357-2947</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0266-5611
ispartof Inverse problems, 2018-08, Vol.34 (8), p.85004
issn 0266-5611
1361-6420
language eng
recordid cdi_iop_journals_10_1088_1361_6420_aac533
source Institute of Physics Journals
subjects asymptotic stability
inverse problem
nonlinear dynamical systems
probability density functions
semi-Markov map
title A new approach to identification of input-driven dynamical systems from probability densities
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T16%3A09%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-iop_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20new%20approach%20to%20identification%20of%20input-driven%20dynamical%20systems%20from%20probability%20densities&rft.jtitle=Inverse%20problems&rft.au=Nie,%20Xiaokai&rft.date=2018-08-01&rft.volume=34&rft.issue=8&rft.spage=85004&rft.pages=85004-&rft.issn=0266-5611&rft.eissn=1361-6420&rft.coden=INPEEY&rft_id=info:doi/10.1088/1361-6420/aac533&rft_dat=%3Ciop_cross%3Eipaac533%3C/iop_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true