Analysis of human-pilot control inputs using neural network
Analysis of human-pilot control inputs using neural network
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DOI：
10.2514/1.16898
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发表时间：
2006-05
影响因子：
2.2
通讯作者：
Shinji Suzuki;Y. Sakamoto;Youhei Sanematsu;H. Takahara
中科院分区：
文献类型：
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作者：
Shinji Suzuki;Y. Sakamoto;Youhei Sanematsu;H. Takahara
应用神经网络建模方法对视觉进场着陆阶段的人-驾驶员控制输入进行了分析。包含飞机状态变量和飞行员控制输入的飞行数据使用飞行模拟器记录。视觉提示和控制输入的时间历史被用作神经网络的教学数据，可以模拟人类飞行员的运动。提出了一种通过确定网络结构和参数初值来提高网络泛化能力的遗传算法。泛化能力是通过分析基于个人计算机的模拟器的飞行数据来评估的。通过使用训练模拟器分析得到的神经网络模型来估计每个视觉线索的贡献率及其对飞行员控制输入的灵敏度。结果表明，该方法可用于分析飞行员的技能。
A neural-network-modeling approach is applied to analyze human-pilot control inputs during the landing phase in the visual approach. Flight data that contain the aircraft state variables and pilot control inputs are recorded using a flight simulator. The time history of visual cues and control inputs is utilized as teaching data for neural networks that can emulate the movements of a human pilot. A genetic algorithms approach is proposed to improve the generalization ability of the network by determining the network structure and initial values of its parameters. Generalization capabilities are evaluated by analyzing the flight data of a personal-computer-based simulator. The contribution ratios of each visual cue and their sensitivities to the control inputs of a pilot are estimated by analyzing the obtained neural-network models using a training simulator. The obtained results reveal that the proposed method can be used for analyzing the skill of a pilot.