基于粗糙集和贝叶斯网络的作战效能评估

Operational Effectiveness Evaluation Based on Rough Set and Bayesian Networks

针对部队作战不确定因素多、建模复杂的特点,提出一种作战行动效能的评估模型。运用粗糙集理论除去冗余的评估指标,降低朴素贝叶斯分类器的时空复杂度。给出该模型的评估算法步骤,通过贝叶斯网络的参数学习,将不同数据类型的评估指标统一在类条件概率分布中,既保证了评估的客观性,又较好地表达出作战过程随机性的特点。实例研究表明,将该方法用于作战行动效能的评估是可行的。

Aiming at the uncertain factors and the complexity of modeling in military operations, an operational effectiveness evaluation model is proposed. Using rough set theory removes redundant evaluation index and reduces the time and space complexity of Naive Bayesian classifier（NB）. An algorithm of the assessment model is put forward. Through the Bayesian network parameter learning, different data types of assessment indexes are expressed in the class conditional probability distribution uniform. This way can not only guarantee the objectivity of the assessment but also express the randomicity of battle process. Instance research shows that the method used to assess the effectiveness of combat operations is feasible.