针对传统“是非制”评判方法过于粗略以及导弹测试数据的不确定性问题, 将导弹状态重新细划为“良好”、“较好”、“堪用”、“拟故障”和“故障”5个等级, 提出了基于云模型和贝叶斯网络的导弹状态评估方法。结合云模型获得性能测试参数与各个状态等级的隶属关系, 采用贝叶斯网络建立多参数状态融合的评估模型, 并且引入DS/AHP方法进行条件概率赋值以减少专家推断过程中的不确定性。

In view of the facts that the traditional “true or false” evaluation method is too rough and the missile test data is uncertain, the state of missiles is re-classified into five levels, namely “perfect”, “good”, “usable”, “pseudo-failed” and “failed”. A missile state evaluation method based on cloud model and Bayesian network is proposed.The cloud model is used to obtain the membership relationship between the performance test parameter and each state level. The Bayesian network is then applied to establish the multi-parameter state fusion evaluation model.The DS/AHP method is introduced to carry out the conditional probability assignment for reducing the uncertainty in the expert inference process.