Approximate entropy as a measure of system complexity. | Zendy

Steven M. Pincus | Zendy

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Approximate entropy as a measure of system complexity.

Author(s) -

Steven M. Pincus

Publication year - 1991

Publication title -

proceedings of the national academy of sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 5.011

H-Index - 771

eISSN - 1091-6490

pISSN - 0027-8424

DOI - 10.1073/pnas.88.6.2297

Subject(s) - approximate entropy , entropy (arrow of time) , chaotic , computer science , correlation dimension , measure (data warehouse) , mathematics , complex system , algorithm , statistical physics , statistics , artificial intelligence , time series , data mining , fractal dimension , fractal , mathematical analysis , physics , quantum mechanics

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.

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