Quantifiers for randomness of chaotic pseudo-random number generators

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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Authors: De Micco, L., Larrondo, H.A., Plastino, A., Rosso, O.A.
Format: article
Status:Published version
Publication Date:2009
Country:Argentina
Institution:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
Repository:Biblioteca Digital (UBA-FCEN)
Language:English
OAI Identifier:paperaa:paper_1364503X_v367_n1901_p3281_DeMicco
Online Access:http://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMicco
Access Level:Open access
Keyword: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
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oai_identifier_str paperaa:paper_1364503X_v367_n1901_p3281_DeMicco
network_acronym_str AR
network_name_str Argentina
repository_id_str
spelling Quantifiers for randomness of chaotic pseudo-random number generatorsDe Micco, L.Larrondo, H.A.Plastino, A.Rosso, O.A.Excess entropyPermutation entropyRandom numberRate entropyRecurrence plotsStatistical complexityChaotic systemsEntropyNumber theoryTime seriesExcess entropyPermutation entropyRandom numberRate entropyRecurrence plotsStatistical complexityRandom number generationarticlenonlinear systemtimeNonlinear DynamicsTime FactorsWe 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.2009info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMiccoPhilos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2009;367(1901):3281-3296reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2024-05-10T10:42:39Zpaperaa:paper_1364503X_v367_n1901_p3281_DeMiccoInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962024-05-10 10:42:40.367Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Quantifiers for randomness of chaotic pseudo-random number generators
title Quantifiers for randomness of chaotic pseudo-random number generators
spellingShingle Quantifiers for randomness of chaotic pseudo-random number generators
De Micco, L.
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
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
dc.creator.none.fl_str_mv De Micco, L.
Larrondo, H.A.
Plastino, A.
Rosso, O.A.
author De Micco, L.
author_facet De Micco, L.
Larrondo, H.A.
Plastino, A.
Rosso, O.A.
author_role author
author2 Larrondo, H.A.
Plastino, A.
Rosso, O.A.
author2_role author
author
author
dc.subject.none.fl_str_mv 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
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
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.
publishDate 2009
dc.date.none.fl_str_mv 2009
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMicco
url http://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMicco
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2009;367(1901):3281-3296
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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score 15.812429

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