Quantifying the complexity of the delayed logistic map

Statistical complexity measures are used to quantify the degree of complexity of the delayed logistic map, with linear and nonlinear feedback. We employ two methods for calculating the complexity measures, one with the 'histogram-based' probability distribution function and the other one w...

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Detalles Bibliográficos
Autores principales: Masoller, C., Rosso, O.A.
Formato: JOUR
Materias:
Complexity
Nonlinear dynamics
Time-delayed systems
Time-series analysis
Distribution functions
Dynamics
Graphic methods
Harmonic analysis
Nonlinear feedback
Time series
Complexity measures
Degree of complexity
Forbidden pattern
Logistic maps
Non-linear dynamics
Ordinal pattern
Parameter regions
Statistical complexity
Time series analysis
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_1364503X_v369_n1935_p425_Masoller
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_1364503X_v369_n1935_p425_Masoller
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spelling todo:paper_1364503X_v369_n1935_p425_Masoller2023-10-03T16:10:57Z Quantifying the complexity of the delayed logistic map Masoller, C. Rosso, O.A. Complexity Nonlinear dynamics Time-delayed systems Time-series analysis Distribution functions Dynamics Graphic methods Harmonic analysis Nonlinear feedback Time series Complexity Complexity measures Degree of complexity Forbidden pattern Logistic maps Non-linear dynamics Ordinal pattern Parameter regions Statistical complexity Time-delayed systems Time series analysis Statistical complexity measures are used to quantify the degree of complexity of the delayed logistic map, with linear and nonlinear feedback. We employ two methods for calculating the complexity measures, one with the 'histogram-based' probability distribution function and the other one with ordinal patterns. We show that these methods provide complementary information about the complexity of the delay-induced dynamics: there are parameter regions where the histogram-based complexity is zero while the ordinal pattern complexity is not, and vice versa. We also show that the time series generated from the nonlinear delayed logistic map can present zero missing or forbidden patterns, i.e. all possible ordinal patterns are realized into orbits. This journal is © 2011 The Royal Society. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_1364503X_v369_n1935_p425_Masoller
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Complexity
Nonlinear dynamics
Time-delayed systems
Time-series analysis
Distribution functions
Dynamics
Graphic methods
Harmonic analysis
Nonlinear feedback
Time series
Complexity
Complexity measures
Degree of complexity
Forbidden pattern
Logistic maps
Non-linear dynamics
Ordinal pattern
Parameter regions
Statistical complexity
Time-delayed systems
Time series analysis
spellingShingle Complexity
Nonlinear dynamics
Time-delayed systems
Time-series analysis
Distribution functions
Dynamics
Graphic methods
Harmonic analysis
Nonlinear feedback
Time series
Complexity
Complexity measures
Degree of complexity
Forbidden pattern
Logistic maps
Non-linear dynamics
Ordinal pattern
Parameter regions
Statistical complexity
Time-delayed systems
Time series analysis
Masoller, C.
Rosso, O.A.
Quantifying the complexity of the delayed logistic map
topic_facet Complexity
Nonlinear dynamics
Time-delayed systems
Time-series analysis
Distribution functions
Dynamics
Graphic methods
Harmonic analysis
Nonlinear feedback
Time series
Complexity
Complexity measures
Degree of complexity
Forbidden pattern
Logistic maps
Non-linear dynamics
Ordinal pattern
Parameter regions
Statistical complexity
Time-delayed systems
Time series analysis
description Statistical complexity measures are used to quantify the degree of complexity of the delayed logistic map, with linear and nonlinear feedback. We employ two methods for calculating the complexity measures, one with the 'histogram-based' probability distribution function and the other one with ordinal patterns. We show that these methods provide complementary information about the complexity of the delay-induced dynamics: there are parameter regions where the histogram-based complexity is zero while the ordinal pattern complexity is not, and vice versa. We also show that the time series generated from the nonlinear delayed logistic map can present zero missing or forbidden patterns, i.e. all possible ordinal patterns are realized into orbits. This journal is © 2011 The Royal Society.
format JOUR
author Masoller, C.
Rosso, O.A.
author_facet Masoller, C.
Rosso, O.A.
author_sort Masoller, C.
title Quantifying the complexity of the delayed logistic map
title_short Quantifying the complexity of the delayed logistic map
title_full Quantifying the complexity of the delayed logistic map
title_fullStr Quantifying the complexity of the delayed logistic map
title_full_unstemmed Quantifying the complexity of the delayed logistic map
title_sort quantifying the complexity of the delayed logistic map
url http://hdl.handle.net/20.500.12110/paper_1364503X_v369_n1935_p425_Masoller
work_keys_str_mv AT masollerc quantifyingthecomplexityofthedelayedlogisticmap
AT rossooa quantifyingthecomplexityofthedelayedlogisticmap
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