Using the memories of multiscale machines to characterize complex systems

被引：3
|
作者：
Jones, Nick S. [1 ,2 ]
机构：
[1] Univ Oxford, Dept Phys, Oxford Ctr Integrat Syst Biol, Oxford OX1 3PU, England
[2] Univ Oxford, Dept Biochem, Oxford Ctr Integrat Syst Biol, Oxford OX1 3PU, England
来源：
PHYSICAL REVIEW LETTERS | 2008年 / 100卷 / 20期
关键词：
D O I：
10.1103/PhysRevLett.100.208702
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
A scheme is presented to extract detailed dynamical signatures from successive measurements of complex systems. Relative entropy based time series tools are used to quantify the gain in predictive power of increasing past knowledge. By lossy compression, data is represented by increasingly coarsened symbolic strings. Each compression resolution is modeled by a machine: a finite memory transition matrix. Applying the relative entropy tools to each machine's memory exposes correlations within many time scales. Examples are given for cardiac arrhythmias and different heart conditions are distinguished.
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页数：4
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