Using Neural Networks and Logistic Regression to Model Small Unmanned Aerial System Accident Results for Risk Mitigation

Sean E. Wolf; Raymond R. Hill; Joseph J. Pignatiello

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Using Neural Networks and Logistic Regression to Model Small Unmanned Aerial System Accident Results for Risk Mitigation

机译：使用神经网络和Logistic回归对小型无人机系统事故结果进行建模以减轻风险

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A flight report dataset for small unmanned aerial system (SUAS) mishaps is analyzed. Mishap contributing factors are identified using artificial neural network (ANN) saliency screening techniques. A failure prediction model is developed using logistic regression to compare with the ANN model and to compute the effects of the contributing factors. SUAS reliability numbers are discussed and statistically significant mishap factors are quantified. Implications for mishap prevention are presented.

机译：分析了小型无人机系统（SUAS）事故的飞行报告数据集。使用人工神经网络（ANN）显着性筛查技术可以识别出可能造成事故的因素。使用逻辑回归开发了故障预测模型，以与ANN模型进行比较并计算影响因素的影响。讨论了SUAS可靠性数字，并对统计上重大的事故因素进行了量化。提出了预防事故的含义。

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《Military operations research》 |2014年第3期|27-39|共13页
作者
Sean E. Wolf; Raymond R. Hill; Joseph J. Pignatiello;
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作者单位

Nellis AFB;

Air Force Institute of Technology;

Air Force Institute of Technology;

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正文语种 eng
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Using Neural Networks and Logistic Regression to Model Small Unmanned Aerial System Accident Results for Risk Mitigation

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