基于BP神经网络的战时维修人员编组评估模型研究

简要介绍了装备保障力量编组的基本方式，分析了维修人员编组的主要影响因素，针对维修人员编组方案评估过程中各因素组合的非线性及评估对象结构的复杂性，提出将维修分队成员同一属性的组合看成维修分队整体的一个属性，并利用BP神经网络对维修分队每个组合属性进行评估，最后利用线性加权法求出维修分队人员编组方案的综合评估结果。以某型火炮维修分队为例对该模型进行了运用，结果表明该模型具有客观、准确、简便的特点。研究结果可对维修人员的优化编组提供理论指导。

Study on Evaluation Model of Maintenance Staff Grouping Based on BP Neural Network in Wartime

This paper concisely introduces the basic methods of equipment support force grouping and analyzes the main influencing factors of maintenance support staff grouping. Aiming at the nonlinear and complexity of factors combination during the evaluation of maintenance staff grouping plan, the combination of the same attribute of maintenance unit members is treated as the attribute of the whole maintenance unit, and BP neural network is applied to get evaluation value of each combined attribute of maintenance unit. At last, comprehensive evaluation value of maintenance unit staff grouping plan is worked out by using linear weighting sum method. Taking some model number artillery maintenance unit as an example, the model is applied. The result suggests this model is objective, accurate and simple. This research result can offer a theoretical guide for optimizing maintenance support staff grouping in wartime.