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在高安全性要求的复杂人机系统中,为了保证系统安全运行,需要对操作员功能状态(Operator Functional State,OFS)进行有效的监测和评估,以防止因操作员状态失效而产生的事故。本文使用基于粒子群优化(Particle Swarm Optimization,PSO)的Wang-Mendel(WM)方法建立起操作员电生理信号与OFS之间的模糊模型,对采用两种不同的规则提取策略的建模结果比较表明,本文使用的混合规则提取策略可以对OFS进行更有效的评估。
In the high safety requirements of complex human-machine systems, in order to ensure the safe operation of the system, the operator functional state (Operator Functional State, OFS) needs to be effectively monitored and evaluated to prevent accidents caused by the failure of the operator. In this paper, we use the Wang-Mendel (WM) method based on Particle Swarm Optimization (PSO) to establish the fuzzy model between operators’ electrophysiological signals and OFS, and compare the modeling results using two different rule extraction strategies It shows that the hybrid rule extraction strategy used in this paper can evaluate OFS more effectively.