Quantifiers for randomness of chaotic pseudo-random number generators

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We deal with randomness quantifiers and concentrate on their ability to discern the hallmark of chaos in time series used in connection with pseudo-random number generators (PRNGs). Workers in the field are motivated to use chaotic maps for generating PRNGs because of the simplicity of their impleme...

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Autores principales: De Micco, L., Larrondo, H.A., Plastino, A., Rosso, O.A.
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: 2009
Materias:
Excess entropy
Permutation entropy
Random number
Rate entropy
Recurrence plots
Statistical complexity
Chaotic systems
Entropy
Number theory
Time series
Random number generation
article
nonlinear system
time
Nonlinear Dynamics
Time Factors
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMicco
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paperaa:paper_1364503X_v367_n1901_p3281_DeMicco
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spelling paperaa:paper_1364503X_v367_n1901_p3281_DeMicco2023-06-12T16:49:53Z Quantifiers for randomness of chaotic pseudo-random number generators Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2009;367(1901):3281-3296 De Micco, L. Larrondo, H.A. Plastino, A. Rosso, O.A. Excess entropy Permutation entropy Random number Rate entropy Recurrence plots Statistical complexity Chaotic systems Entropy Number theory Time series Excess entropy Permutation entropy Random number Rate entropy Recurrence plots Statistical complexity Random number generation article nonlinear system time Nonlinear Dynamics Time Factors We deal with randomness quantifiers and concentrate on their ability to discern the hallmark of chaos in time series used in connection with pseudo-random number generators (PRNGs). Workers in the field are motivated to use chaotic maps for generating PRNGs because of the simplicity of their implementation. Although there exist very efficient general-purpose benchmarks for testing PRNGs, we feel that the analysis provided here sheds additional didactic light on the importance of the main statistical characteristics of a chaotic map, namely (i) its invariant measure and (ii) the mixing constant. This is of help in answering two questions that arise in applications: (i) which is the best PRNG among the available ones? and (ii) if a given PRNG turns out not to be good enough and a randomization procedure must still be applied to it, which is the best applicable randomization procedure? Our answer provides a comparative analysis of several quantifiers advanced in the extant literature. © 2009 The Royal Society. 2009 info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/publishedVersion application/pdf eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMicco
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
language Inglés
orig_language_str_mv eng
topic Excess entropy
Permutation entropy
Random number
Rate entropy
Recurrence plots
Statistical complexity
Chaotic systems
Entropy
Number theory
Time series
Excess entropy
Permutation entropy
Random number
Rate entropy
Recurrence plots
Statistical complexity
Random number generation
article
nonlinear system
time
Nonlinear Dynamics
Time Factors
spellingShingle Excess entropy
Permutation entropy
Random number
Rate entropy
Recurrence plots
Statistical complexity
Chaotic systems
Entropy
Number theory
Time series
Excess entropy
Permutation entropy
Random number
Rate entropy
Recurrence plots
Statistical complexity
Random number generation
article
nonlinear system
time
Nonlinear Dynamics
Time Factors
De Micco, L.
Larrondo, H.A.
Plastino, A.
Rosso, O.A.
Quantifiers for randomness of chaotic pseudo-random number generators
topic_facet Excess entropy
Permutation entropy
Random number
Rate entropy
Recurrence plots
Statistical complexity
Chaotic systems
Entropy
Number theory
Time series
Excess entropy
Permutation entropy
Random number
Rate entropy
Recurrence plots
Statistical complexity
Random number generation
article
nonlinear system
time
Nonlinear Dynamics
Time Factors
description We deal with randomness quantifiers and concentrate on their ability to discern the hallmark of chaos in time series used in connection with pseudo-random number generators (PRNGs). Workers in the field are motivated to use chaotic maps for generating PRNGs because of the simplicity of their implementation. Although there exist very efficient general-purpose benchmarks for testing PRNGs, we feel that the analysis provided here sheds additional didactic light on the importance of the main statistical characteristics of a chaotic map, namely (i) its invariant measure and (ii) the mixing constant. This is of help in answering two questions that arise in applications: (i) which is the best PRNG among the available ones? and (ii) if a given PRNG turns out not to be good enough and a randomization procedure must still be applied to it, which is the best applicable randomization procedure? Our answer provides a comparative analysis of several quantifiers advanced in the extant literature. © 2009 The Royal Society.
format Artículo
Artículo
publishedVersion
author De Micco, L.
Larrondo, H.A.
Plastino, A.
Rosso, O.A.
author_facet De Micco, L.
Larrondo, H.A.
Plastino, A.
Rosso, O.A.
author_sort De Micco, L.
title Quantifiers for randomness of chaotic pseudo-random number generators
title_short Quantifiers for randomness of chaotic pseudo-random number generators
title_full Quantifiers for randomness of chaotic pseudo-random number generators
title_fullStr Quantifiers for randomness of chaotic pseudo-random number generators
title_full_unstemmed Quantifiers for randomness of chaotic pseudo-random number generators
title_sort quantifiers for randomness of chaotic pseudo-random number generators
publishDate 2009
url http://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMicco
work_keys_str_mv AT demiccol quantifiersforrandomnessofchaoticpseudorandomnumbergenerators
AT larrondoha quantifiersforrandomnessofchaoticpseudorandomnumbergenerators
AT plastinoa quantifiersforrandomnessofchaoticpseudorandomnumbergenerators
AT rossooa quantifiersforrandomnessofchaoticpseudorandomnumbergenerators
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