Machine learningNonlinear dynamics
样本熵
想象一下阅读一长串字母，然后问：如果两个短语看起来相似，那么下一个字母匹配的频率有多高？如果下一个字符几乎总是匹配，那么序列就非常可预测，熵值较低。如果很少匹配——序列不可预测且复杂——熵值就较高。样本熵计算这种匹配扩展的次数，排除自我匹配以避免偏差，然后将比率转换为单一的复杂性得分。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, 278(6), H2039–H2049. DOI: 10.1152/ajpheart.2000.278.6.H2039 ↗
如何引用本页
ScholarGate. (2026, June 2). Sample Entropy (Time-Series Complexity). ScholarGate. https://scholargate.app/zh/complex-systems/sample-entropy
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →