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Research on Interval Estimation of Trip Fuel Consumption Based on Irregularly Distributed Samples

机译:基于不规则分布式样本的跳闸燃料消耗区间估计研究

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Interval estimation of aircraft fuel consumption is an important basis for airlines to make flight plans and to save energy and reduce emissions. Aiming at the problem that the fuel consumption samples under the influence of multiple factors presented irregular distribution, which would reduce the rationality and accuracy of fuel consumption estimation, an interval estimation method of trip fuel consumption based on the combination of support vector quantile regression and Bootstrap (SVQR-Bootstrap) was proposed. The method based on quantile regression residual error minimization of the absolute value of asymmetric form thoughts to obtain the conditional quantile of trip fuel consumption and on this basis, through the Bootstrap estimate mean and standard deviation of trip fuel consumption population, and then, according to fixed aircraft type and segment, the population of fuel consumption meets the normal distribution, building estimated interval of trip fuel consumption under a constant confidence level. According to simulation comparisons, the SVQR-Bootstrap method can effectively reduce the length of the estimation range, pointing to the clear, which is better than the interval estimation method which requires assumed data distribution.
机译:飞机燃料消耗的间隔估计是航空公司制作飞行计划并节省能源和减少排放的重要基础。针对燃料消耗样本在多个因素的影响下提出了不规则分布的问题,这将降低燃料消耗估计的合理性和准确性,基于支持向量分位数回归和自举的组合来降低避险燃料消耗的间隔估计方法(SVQR-Bootstrap)提出。基于量子回归剩余误差的方法最小化不对称形式思想的绝对值,以获得跳闸燃料消耗的条件分位数,在此基础上,通过自举估计速度和标准偏差的跳闸燃料消耗群,然后,根据固定飞机类型和段,燃料消耗群体满足正常分布,在恒定的置信水平下建立估计的跳闸燃料消耗间隔。根据仿真比较,SVQR-Bootstrap方法可以有效地降低估计范围的长度,指向清晰,这比需要采用假定数据分布的间隔估计方法更好。

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