Method for Analysis of Non-stationarity of Fluctuating Winds Based on Revised Local Recurrence Rate
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摘要: 为了克服递归趋势(recurrence trend, RT)指标对不同信号非平稳度估计存在误判的缺陷,分别采用互信息法和伪临近法确定了递归量化分析的最佳延迟时间和最小嵌入维数,然后在递归量化分析基础上,提出了归一化局部递归率标准差(standard deviation of normalized local recurrence rate, SDNLRR)作为信号非平稳度量化指标.利用该指标,通过递归量化分析方法分析了白噪声信号、正弦信号、调幅信号、线性调频信号4个基本信号和2个实测台风场脉动风速信号的非平稳特性,并与传统的递归趋势指标分析结果进行了对比.研究结果表明:利用SDNLRR指标对6个信号的非平稳度的量化比较准确率达100%,比RT指标的准确率提高了33.33%,消除了RT指标对正弦信号和平稳脉动风速信号的错误估计.Abstract: In order to overcome the misestimate of the non-stationarity of different signals by the recurrence trend (RT), the mutual information function and false nearest neighbors are employed to determine the time delay and minimum embedding dimension of the recurrence quantification analysis (RQA), respectively. Then a novel index, i.e., the standard deviation of normalized local recurrence rate (SDNLRR), which is based on the RQA, was proposed to quantify the non-stationary degree of the signals. Utilizing the SDNLRR, the non-stationarity of four basic signals (white noise signal, sinusoidal signal, amplitude-modulated signal and linear frequency modulation signal) and two field-observed fluctuating wind speed histories were analyzed and compared with the analysis results of RT. The results show that the proposed SDNLRR could offer a quantitative comparison of the non-stationarity of the above six signals with a 100% accuracy. The new method eliminates the misestimates of the sinusoidal signal and the stationary fluctuating wind speed signal in RT, and hence is more accurate than the RT estimation by 33.33%.
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