基于动态贝叶斯网络和模糊灰度理论的飞行训练评估

针对战斗机飞行训练中的评估问题，提出了一种基于动态贝叶斯网络和模糊灰度理论的评估方法。首先，分析了训练过程中典型飞行参数与机动动作的因果关系，根据专家经验与先验知识构建基于动态贝叶斯网络的机动动作识别模型，推理得到战斗机机动动作识别结果。然后，建立战斗机飞行训练评估指标体系，根据战斗机机动识别结果选择飞行训练评估指标，并采用综合赋权法确定了指标权重。最后，建立灰度模糊评估矩阵，结合飞行训练过程中各评估指标的飞行数据得到评估结果。实验结果表明该评估方法能够根据飞行过程中的参数信息进行机动动作识别及飞行训练评估，提高了飞行训练评估的效率。

Flight training evaluation based on dynamic Bayesian network and fuzzy gray theory

An evaluation method for flight training of warplanes is proposed based on dynamic Bayesian network and fuzzy gray theory. Firstly, the causal relationship between typical flight parameters and maneuver in the training process is analyzed. The maneuver recognition model based on dynamic Bayesian network is constructed according to expert experience and prior knowledge, and the maneuver recognition results are obtained by reasoning. Then, an evaluation index system of fighter flight training is established. The evaluation index of flight training is selected according to the results of fighter maneuver identification, and the index weight is determined by the comprehensive weighting method. Finally, the gray fuzzy evaluation matrix is established, and the evaluation results are obtained by calculating the flight data of each evaluation index in the flight training process. The experimental results show that the evaluation method proposed can recognize maneuver and evaluate flight training according to the parameters in the flight process, improving the efficiency of flight training evaluation.