摘要
为揭示起飞流的内在动态特性,利用非线性方法对起飞流混沌特性进行识别。基于混沌理论对起飞流时间序列进行相空间重构,利用c-c算法计算时间延迟和嵌入维数,通过小数据量法计算最大Lyapunov指数来判别起飞流时间序列的混沌特性。分析了起飞流数据样本量大小和数据中含有的噪声对起飞流可预测性的影响。以北京首都机场实际运行数据为例进行分析,通过实测数据计算,结果表明:采样时间为60 min、30 min、15 min和10 min这4种观测尺度的起飞流时间序列均具有混沌特性;起飞流的可预测性受到样本数据大小和噪声的影响。在相同的预测步长下,北京机场起飞流的可预测性随着样本数据量的增加而减小;而噪声则降低了北京机场起飞流的可预测性。
In order to reveal the internal dynamic property of take-off flow,the nonlinear analysis method is used to identify the chaotic property of take-off flow. Take-off flow time series is reconstructed in phase-space based on chaos theory. The embedding dimension and delay time are calculated via the c-c method. The largest Lyapunov exponent of take-off flow is calculated on the basis of small data set method to verify the presence of chaos in the take-off flow. Finally,the influence of the sample size of the take-off flow and the noise contained in the data on the predictability of the take-off flow is analyzed. Taking Beijing Capital Airport actual operation data as an example for analysis,calculated from the measured data,the results show that chaotic properties in different statistical scales of 60,30,15 and 10 min. the predictability of the take-off flow is affected by the size of the data and the noise of the sample. At the same forecasting step,the predictability of the take-off flow at Beijing airport decreases with the increase of sample data amount; while the noise reduces the predictability of take-off flow at Beijing airport.
作者
张勰
刘伟
赵嶷飞
ZHANG Xie;LIU Wei;ZHAO Yi-fei(College of Air Traffic Management,Civil Aviation University of China,Tianjin 300300,China)
出处
《航空计算技术》
2018年第3期36-40,共5页
Aeronautical Computing Technique
基金
国家自然科学基金项目资助(U1633112
U1533112)
国家重点研发计划项目资助(2016YFB0502400)
