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于 2013-05-17 发布文件大小:1KB

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近似熵是一种衡量衡量非线性时间序列复杂性的非线性动力学分析方法，其物理意义为熵值越大，时间序列的自我相似性越低，序列越复杂，反之序列自我相似性越高；并且样本熵对于序列长度依赖弱，仅需较短的数据就能够得到稳健的熵值，同时其计算不需对数据粗粒化，具有较好的抗干扰能力 (Approximate entropy is a measure to measure the complex nonlinear dynamics of nonlinear time series analysis method, the physical meaning of entropy value is larger, the lower the self-similarity of the time series, the more complex the sequence, whereas sequence of self-similarity the higher and the sample entropy weak dependence for the length of the sequence, only shorter data can be healthy entropy value, calculated without data coarse-grained, with good anti-jamming capability)

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