基于模糊逻辑的人为差错概率量化方法

为满足人机系统概率风险评估的需要，提出一种人为差错概率量化方法。分析技能、规则和知识为基础 (skill, rule and knowledge-based，SRK)框架和行为模式的确定方法Hanaman 决策树法，指出在确定行为模式的过程 中考虑行为模式影响因素的不确定性是必要的；使用模糊逻辑方法处理行为模式各个影响因素的不确定性，根据 Hanaman 决策树构建模糊推理规则，利用系统人为行为可靠性程序(systematic human action reliability procedure, SHARP)方法所提供的人为差错概率区间确定人为差错概率的隶属度函数。结果表明：该方法考虑了任务场景的不确 定性，可以得到人为差错概率的精确值，满足人机系统概率风险评估的需要。

Quantification Method of Human Error Probability Based on Fuzzy Logic

In order to meet the needs of probabilistic risk assessment of man-machine system, a quantitative method of human error probability is proposed. Skill, rule and knowledge-based (SRK) framework and Hanaman decision tree method for behavior pattern determination are analyzed. It is pointed out that it is necessary to consider the uncertainty of the influencing factors of behavior pattern in the process of behavior pattern determination; The fuzzy logic method is used to deal with the uncertainty of each influencing factor of the behavior pattern, and the fuzzy reasoning rules are constructed according to the Hanaman decision tree. The membership function of human error probability is determined by using the human error probability interval provided by systematic human action reliability procedure (SHARP) method. The results show that the uncertainty of the task scenario is considered in the method, and the accurate value of the human error probability can be obtained, which meets the needs of the probabilistic risk assessment of the man-machine system.