Statistical Complexity of Chaotic Pseudorandom Number Generators
- Authors: Hilda A. Larrondo1, Luciana De Micco2, Claudio M. Gonzalez3, Angelo Plastino4, Osvaldo A. Rosso5
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View Affiliations Hide Affiliations1 Facultad de Ingenier´ıa, Universidad Nacional de Mar del Plata, Juan B. Justo 4302, 7600 Mar del Plata, Argentina 2 Facultad de Ingenier´ıa, Universidad Nacional de Mar del Plata, Juan B. Justo 4302, 7600 Mar del Plata, Argentina 3 Facultad de Ingenier´ıa, Universidad Nacional de Mar del Plata, Juan B. Justo 4302, 7600 Mar del Plata, Argentina 4 Instituto de F´ısica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata C.C. 727, 1900 La Plata, Argentina 5 Departamento de F´ısica, Instituto de Ciˆencias Exatas, Universidade Federal de Minas Gerais Av. Antˆonio Carlos, 6627 - Campus Pampulha. 31270-901 Belo Horizonte - MG, Brazil
- Source: Concepts and Recent Advances in Generalized Information Measures and Statistics , pp 283-308
- Publication Date: December 2013
- Language: English
Statistical Complexity of Chaotic Pseudorandom Number Generators, Page 1 of 1
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This chapter deals with the use of the Statistical Complexity Measure, as defined by Lopez Ruiz, Mancini and Calbet [Phys. Lett. A 209 (1995) 321326] and modified by Rosso and coworkers [P. W. Lamberti, M. T. Martin, A. Plastino, O. A. Rosso; Physica A 334 (2004) 119131] to characterize pseudo random number generators (PRNG's) obtained from chaotic dynamical systems. It is shown that two probability distribution functions are required for a proper characterization: the first one is based on the histogram and is used to characterize the uniformity of the values in the time series; the second one is based on the permutation procedure proposed by Bandt and Pompe [Phys. Rev. Lett. 88 (2002) 174102] and characterize the uniformity of patterns of several consecutive values of the time series.
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