电光与控制, 2016, 23(11): 78, 网络出版: 2016-11-01

基于FPA优化的GP算法的飞行员认知状态识别

Recognition of Pilot's Cognitive State Based on FPA Optimized GP
作者单位

1上海交通大学, 航空航天学院,上海 200240

2上海交通大学, 电子信息与电气工程学院,上海 200240

3上海交通大学,电子信息与电气工程学院,上海 200240

摘要
飞行员认知状态是影响人机飞行控制系统表现的重要因素。认知状态通常不能被直接测得, 需借助于诸多的生理信号间接分析。根据典型生理信号的时频特点, 应用小波分析建立信号特征集, 并提出了一种基于花粉传播算法的高斯过程分类模型, 用以分析全动飞行模拟实验中的飞行员认知状态。通过对比分类结果与飞行员的NASA-TLX测评结果, 验证该模型对飞行员认知状态识别的有效性。
Abstract
The pilots'cognitive states are essential factors affecting their control performance to the flight control system. Generally, cognitive states can not be measured directly. However, they can be gained indirectly by the means of physiological signals. Based on the characteristics of the physiological signals in time frequency domain, wavelet analysis is used for establishing the feature sets, and an FPA optimized Gaussian Process (GP) model is proposed for classification based on flower pollination algorithm, which is used for analyzing the pilots'cognitive states in a full flight simulation. Through the comparison between the classification results and the NASA-TLX test result of the pilots, the validity of this method is verified.
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