Journal of Atomic, Molecular, and Optical Physics
Volume 2012 (2012), Article ID 589651, 6 pages
Statistical Complexity of Low- and High-Dimensional Systems
1Department of Complex System, School of Systems Information Science, Future University Hakodate, 116-2 Kamedanakano-Cho, Hakodate-Shi, Hakodate, Hokkaido 041-8655, Japan
2Non-Linearity and Complexity Research Group, Aston University, Birmingham B4 7ET, UK
Received 2 January 2012; Revised 5 April 2012; Accepted 5 April 2012
Academic Editor: Keli Han
Copyright © 2012 Vladimir Ryabov and Dmitry Nerukh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We suggest a new method for the analysis of experimental time series that can distinguish high-dimensional dynamics from stochastic motion. It is based on the idea of statistical complexity, that is, the Shannon entropy of the so-called ϵ-machine (a Markov-type model of the observed time series). This approach has been recently demonstrated to be efficient for making a distinction between a molecular trajectory in water and noise. In this paper, we analyse the difference between chaos and noise using the Chirikov-Taylor standard map as an example in order to elucidate the basic mechanism that makes the value of complexity in deterministic systems high. In particular, we show that the value of statistical complexity is high for the case of chaos and attains zero value for the case of stochastic noise. We further study the Markov property of the data generated by the standard map to clarify the role of long-time memory in differentiating the cases of deterministic systems and stochastic motion.