基于自组织算法与回归分析结合方法的飞行员脑力负荷预测研究

Prediction of Pilot's Mental Workload Based on GMDH and Regression Analysis

脑力负荷是人机系统中人的绩效的一个重要因素。对飞行员脑力负荷展开研究,为飞机驾驶舱设计及其仪表设备的符合性验证提供参考。通过实验得到生理测量、绩效测量、主观测量的各项指标。利用单因素方差分析法提取对飞行员脑力负荷的敏感指标。结果表明:注视频率、注视总时间、眨眼率、平均瞳孔直径变化率、NASA_TLX(NASA task load index)、正确率的主效应显著(P<0.05)。采用自组织算法GMDH(group method of data handling)与线性回归的结合方法,建立飞行员脑力负荷预测模型;并且得到模型拟合度为85.47%。因此,GMDH与线性回归的结合方法可以较好地预测飞行员脑力负荷。

Mental workload is one of the most important factors in the performance of human and machine system,The reference for the cockpit design and the instrument verification can be provided by the research on the pilot's mental workload. The indexes of physiological measurement,performance measurement and subjective measurement were obtained by experiment. The mental workload sensitive index obtained by single factor analysis of variance,and the results showed that: the main effects of fixation frequency,total fixation time,blink rate,average pupil diameter,NASA_TLX and correct rate were significant( P < 0. 05). In this paper,combination research on GMDH and regression methods was used to establish the prediction model of pilots' mental workload,and the goodness of fit of the model was 85. 47%. Therefore,this method can be used to predict the mental workload of pilots.