Approximate entropy as a measure of system complexity.

Steven M. Pincus · Proceedings of the National Academy of Sciences · 1991

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Steven M. Pincus

Publisher

Proceedings of the National Academy of Sciences

Year

1991

Language

English

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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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APA 7

Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. https://doi.org/10.1073/pnas.88.6.2297

MLA

Pincus, Steven M. "Approximate entropy as a measure of system complexity." 1991. https://doi.org/10.1073/pnas.88.6.2297.

Chicago

Pincus, Steven M. 1991. "Approximate entropy as a measure of system complexity.". https://doi.org/10.1073/pnas.88.6.2297.

Harvard

Pincus, S. M. 1991, Approximate entropy as a measure of system complexity, Proceedings of the National Academy of Sciences, available at: https://doi.org/10.1073/pnas.88.6.2297 [Accessed 7 Aug. 2026].

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Title: Approximate entropy as a measure of system complexity.

Author / contributors: Steven M. Pincus

Publisher: Proceedings of the National Academy of Sciences

Publication year: 1991

Language: English

Subjects

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Approximate entropy; Entropy (arrow of time); Chaotic; Computer science; Correlation dimension; Measure (data warehouse); Mathematics; Complex system; Algorithm; Statistical physics; Statistics; Artificial intelligence