View in EDS

Assessment of cooperativity in complex systems with non-periodical dynamics: Comparison of five mutual information metrics.

Saved in:
Bibliographic Details
Title: Assessment of cooperativity in complex systems with non-periodical dynamics: Comparison of five mutual information metrics.
Authors: Pyko, Nikita S.1, Pyko, Svetlana A.1, Markelov, Oleg A.1, Karimov, Artur I.1, Butusov, Denis N.1, Zolotukhin, Yaroslav V.1, Uljanitski, Yuri D.1, Bogachev, Mikhail I.1 rogex@yandex.com
Source: Physica A. Aug2018, Vol. 503, p1054-1072. 19p.
Subjects: Stochastic processes, Dynamical systems, Randomization (Statistics), Phase transitions, Autocorrelation (Statistics), Chaos theory
Abstract: Abstract Quantitative assessment of cooperativity effects is essential for a better understanding of the interactions between system components that is an important step on the way from black-box to structural models of various complex dynamical systems. In this paper, we consider five widely used mutual information metrics and test their performance using simulated stochastic data series with introduced phase- or amplitude randomization as well as data series generated by chaotic maps. We show the performance of all studied methods in both stationary mode and during phase transitions, indicating specific coupling patterns they can reveal from the system dynamics, as well as certain properties they appear invariant to. Finally, we demonstrate how a combination of several metrics can be used for a more detailed analysis of dynamical systems exhibiting characteristic phase transitions, including examples of both simulated chaotic maps and observational data series from physiological and geophysical complex systems. Highlights • Cooperativity metrics in complex systems are affected by both cross- and autocorrelations. • Time delay stability is unaffected by additive while affected by multiplicative white noise. • Coherence is invariant to short-term, while cross-conditional entropy to long-term correlations. • Combination of several metrics provide a better characterization of phase transitions. • Phase transition in geomagnetic network reveals a characteristic pattern during a solar flare. [ABSTRACT FROM AUTHOR]
Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 133191669
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Assessment of cooperativity in complex systems with non-periodical dynamics: Comparison of five mutual information metrics.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Pyko%2C+Nikita+S%2E%22">Pyko, Nikita S.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pyko%2C+Svetlana+A%2E%22">Pyko, Svetlana A.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Markelov%2C+Oleg+A%2E%22">Markelov, Oleg A.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Karimov%2C+Artur+I%2E%22">Karimov, Artur I.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Butusov%2C+Denis+N%2E%22">Butusov, Denis N.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zolotukhin%2C+Yaroslav+V%2E%22">Zolotukhin, Yaroslav V.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Uljanitski%2C+Yuri+D%2E%22">Uljanitski, Yuri D.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bogachev%2C+Mikhail+I%2E%22">Bogachev, Mikhail I.</searchLink><relatesTo>1</relatesTo><i> rogex@yandex.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Physica+A%22">Physica A</searchLink>. Aug2018, Vol. 503, p1054-1072. 19p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamical+systems%22">Dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Randomization+%28Statistics%29%22">Randomization (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+transitions%22">Phase transitions</searchLink><br /><searchLink fieldCode="DE" term="%22Autocorrelation+%28Statistics%29%22">Autocorrelation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Chaos+theory%22">Chaos theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Abstract Quantitative assessment of cooperativity effects is essential for a better understanding of the interactions between system components that is an important step on the way from black-box to structural models of various complex dynamical systems. In this paper, we consider five widely used mutual information metrics and test their performance using simulated stochastic data series with introduced phase- or amplitude randomization as well as data series generated by chaotic maps. We show the performance of all studied methods in both stationary mode and during phase transitions, indicating specific coupling patterns they can reveal from the system dynamics, as well as certain properties they appear invariant to. Finally, we demonstrate how a combination of several metrics can be used for a more detailed analysis of dynamical systems exhibiting characteristic phase transitions, including examples of both simulated chaotic maps and observational data series from physiological and geophysical complex systems. Highlights • Cooperativity metrics in complex systems are affected by both cross- and autocorrelations. • Time delay stability is unaffected by additive while affected by multiplicative white noise. • Coherence is invariant to short-term, while cross-conditional entropy to long-term correlations. • Combination of several metrics provide a better characterization of phase transitions. • Phase transition in geomagnetic network reveals a characteristic pattern during a solar flare. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=133191669
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.physa.2018.08.146
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 1054
    Subjects:
      – SubjectFull: Stochastic processes
        Type: general
      – SubjectFull: Dynamical systems
        Type: general
      – SubjectFull: Randomization (Statistics)
        Type: general
      – SubjectFull: Phase transitions
        Type: general
      – SubjectFull: Autocorrelation (Statistics)
        Type: general
      – SubjectFull: Chaos theory
        Type: general
    Titles:
      – TitleFull: Assessment of cooperativity in complex systems with non-periodical dynamics: Comparison of five mutual information metrics.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Pyko, Nikita S.
      – PersonEntity:
          Name:
            NameFull: Pyko, Svetlana A.
      – PersonEntity:
          Name:
            NameFull: Markelov, Oleg A.
      – PersonEntity:
          Name:
            NameFull: Karimov, Artur I.
      – PersonEntity:
          Name:
            NameFull: Butusov, Denis N.
      – PersonEntity:
          Name:
            NameFull: Zolotukhin, Yaroslav V.
      – PersonEntity:
          Name:
            NameFull: Uljanitski, Yuri D.
      – PersonEntity:
          Name:
            NameFull: Bogachev, Mikhail I.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: Aug2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 03784371
          Numbering:
            – Type: volume
              Value: 503
          Titles:
            – TitleFull: Physica A
              Type: main
ResultId 1