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arXiv 0802.1138cond-mat.stat-mech
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混沌边缘Logistic映射的时间平均

A closer look at time averages of the logistic map at the edge of chaos

Ugur Tirnakli, Constantino Tsallis, Christian Beck

摘要:

最近已经证明了在混沌边缘的Logistic映射的迭代和的概率分布[见U。蒂尔纳克利角Beck和C. Tsallis,Phys.Rev.E75,040106(R)(2007)]在数值上与q-高斯分布一致,该分布在适当的约束下使非加性熵S_q最大化,非广延统计力学的基础。这一分析基于对分布尾部的研究。我们现在检查整个分布,特别是它的中心部分。这一点很重要,因为最近q-推广的中心极限定理指出,对于某些类强相关的随机变量,重标度和接近q-高斯极限分布。我们数值研究的Logistic映射的参数在一个小的临界点附近的条件下,有收敛到一个q-高斯在中心区域和尾部区域,并找到一个涉及Feigenbaum常数δ的标度律。我们的结果是一致的大量已经可用的分析和数值证据,混沌的边缘是很好地描述熵S_q及其相关的概念。

英文摘要:

The probability distribution of sums of iterates of the logistic map at the edge of chaos has been recently shown [see U. Tirnakli, C. Beck and C. Tsallis, Phys. Rev. E 75, 040106(R) (2007)] to be numerically consistent with a q-Gaussian, the distribution which, under appropriate constraints, maximizes the nonadditive entropy S_q, the basis of nonextensive statistical mechanics. This analysis was based on a study of the tails of the distribution. We now check the entire distribution, in particular its central part. This is important in view of a recent q-generalization of the Central Limit Theorem, which states that for certain classes of strongly correlated random variables the rescaled sum approaches a q-Gaussian limit distribution. We numerically investigate for the logistic map with a parameter in a small vicinity of the critical point under which conditions there is convergence to a q-Gaussian both in the central region and in the tail region, and find a scaling law involving the Feigenbaum constant delta. Our results are consistent with a large number of already available analytical and numerical evidences that the edge of chaos is well described in terms of the entropy S_q and its associated concepts.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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