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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2009
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_1364503X_v367_n1901_p3281_DeMicco http://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMicco |
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paper:paper_1364503X_v367_n1901_p3281_DeMicco2025-07-30T18:47:27Z Quantifiers for randomness of chaotic pseudo-random number generators 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 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_1364503X_v367_n1901_p3281_DeMicco 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) |
| 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 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. |
| 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 |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_1364503X_v367_n1901_p3281_DeMicco http://hdl.handle.net/20.500.12110/paper_1364503X_v367_n1901_p3281_DeMicco |
| _version_ |
1840321640576057344 |