近似熵作为系统复杂性的一种度量。
Approximate entropy as a measure of system complexity.
作者信息
Pincus S M
机构信息
990 Moose Hill Road, Guilford, CT 06437, USA.
出版信息
Proc Natl Acad Sci U S A. 1991 Mar 15;88(6):2297-301. doi: 10.1073/pnas.88.6.2297.
Techniques to determine changing system complexity from data are evaluated. Convergence of a frequently used correlation dimension algorithm to a finite value does not necessarily imply an underlying deterministic model or chaos. Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes. The capability to discern changing complexity from such a relatively small amount of data holds promise for applications of ApEn in a variety of contexts.
对从数据中确定系统复杂性变化的技术进行了评估。常用的关联维算法收敛到有限值并不一定意味着存在潜在的确定性模型或混沌。对最近开发的一组公式和统计量——近似熵（ApEn）的分析表明，在包括确定性混沌和随机过程的各种不同设置中，给定至少1000个数据值时，ApEn可以对复杂系统进行分类。从如此相对少量的数据中辨别复杂性变化的能力为ApEn在各种情况下的应用带来了希望。
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