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Multifractal properties of elementary cellular automata in a discrete wavelet approach of MF-DFA

机译:MF-DFA离散小波方法中基本元胞自动机的多重分形特性

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In 2005, Nagler and Claussen (Phys. Rev. E, 71 (2005) 067103) investigated the time series of the elementary cellular automata (ECA) for possible (multi)fractal behavior. They eliminated the polynomial background at(b) through the direct fitting of the polynomial coefficients a and b. We here reconsider their work eliminating the polynomial trend by means of the multifractal-based detrended fluctuation analysis (MF-DFA) in which the wavelet multiresolution property is employed to filter out the trend in a more speedy way than the direct polynomial fitting and also with respect to the wavelet transform modulus maxima (WTMM) procedure. In the algorithm, the discrete fast wavelet transform is used to calculate the trend as a local feature that enters the so-called details signal. We illustrate our result for three representative ECA rules: 90, 105, and 150. We confirm their multifractal behavior and provide our results for the scaling parameters. Copyright (C) EPLA, 2009
机译:在2005年,Nagler和Claussen(Phys。Rev. E,71(2005)067103)研究了基本元胞自动机(ECA)可能的(多重)分形行为的时间序列。他们通过直接拟合多项式系数a和b消除了(b)处的多项式背景。我们在这里重新考虑他们的工作,通过基于多重分形的去趋势波动分析(MF-DFA)消除多项式趋势,在该分析中,与直接多项式拟合相比,采用小波多分辨率特性可以更快地滤除趋势,并且关于小波变换模极大值(WTMM)程序。在该算法中,离散快速小波变换用于将趋势计算为输入所谓的细节信号的局部特征。我们举例说明了三个代表性ECA规则的结果:90、105和150。我们确认了它们的多重分形行为,并提供了缩放参数的结果。版权所有(C)EPLA,2009年

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