Self-similar motion for modeling anomalous diffusion and nonextensive statistical distributions
Abstract
We introduce a new universality class of one-dimensional iteration model giving rise to self-similar motion, in which the Feigenbaum constants are generalized as self-similar rates and can be predetermined. The curves of the mean-square displacement versus time generated here show that the motion is a kind of anomalous diffusion with the diffusion coefficient depending on the self-similar rates. In addition, it is found that the distribution of displacement agrees to a reliable precision with the q-Gaussian type distribution in some cases and bimodal distribution in some other cases. The results obtained show that the self-similar motion may be used to describe the anomalous diffusion and nonextensive statistical distributions.
Cite
@article{arxiv.1001.2880,
title = {Self-similar motion for modeling anomalous diffusion and nonextensive statistical distributions},
author = {Zhifu Huang and Guozhen Su and Qiuping A Wang and Jincan Chen},
journal= {arXiv preprint arXiv:1001.2880},
year = {2010}
}
Comments
15pages, 5figures