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为有效利用径流数据的非线性特征来对径流作分类识别,提出采用递归图和近似熵获取原始时间序列的动力学行为的方法.在计算随机、正弦和Logistic映射3种典型时间序列的递归图的基础上,采用递归图分析获取不同时间尺度的径流时间序列的动力学行为的方法,进行确定性检验,定性的判断径流序列的非线性和原动力系统的确定性.在递归图算法的基础上,定量地将近似熵用于序列的复杂度描述,对4组径流序列的递归图和近似熵进行比较,得出混沌递归分析是一种流型识别的有效辅助诊断工具.
In order to effectively use the nonlinear characteristics of runoff data to classify and recognize runoff, a method of obtaining the dynamic behavior of the original time series using recursive graphs and approximate entropies is proposed.In the calculation of recursive graphs of three typical time series of random, sinusoidal and Logistic maps , The recursive graph analysis is used to obtain the dynamic behavior of runoff time series on different time scales and the deterministic test is used to qualitatively determine the nonlinearity of the runoff series and the certainty of the dynamical system.On the basis of the recursive graph algorithm , The approximate entropy is quantitatively used for the description of the complexity of the sequence, and the recursive graphs and approximate entropies of the four runoff sequences are compared. It is concluded that chaos recursive analysis is an effective auxiliary diagnostic tool for flow pattern recognition.