油料保障预测系统中案例推理方法

一般油料保障需求预测都采用历史数据回归分析的方法,这些方法无法适用缺少油料消耗历史数据的突然性的部队行动样式。为解决此问题,引入了人工智能领域的案例推理技术,采用层次分析法计算油料保障需求案例各特征属性权重;针对油料保障需求案例属性缺失的普遍问题,提出了一种基于结构可比度和属性相似度的二次案例相似度计算方法。最后模拟某坦克团的一次军事行动油料保障需求预测对上述理论和方法进行了仿真和验证,仿真结果表明,案例推理技术在油料保障需求预测中能够在缺少历史数据的情况下进行比较精确的预测。

Case-Based Reasoning in Oil Support Forecasting System

Generally,the oil support demand is forecasted through the method of historical data regression analysis, these methods become not so effective when confront with the situations of sudden force action style which are short of the historical oil consumption data.In order to solve this problem,case-based reasoning techniques is introduced,then the oil support demand cases property weights are calculated by the analytic hierarchy process;Aiming at a universal problem that missing of some case properties,a similarity calculating method called "twice-calculating" is proposed which base on the structure comparable degree and the similarity degree.Finally the theory and the method proposed above are simulated and verified by a case of oil support demand forecasting of a tank regiment’s military action.The result shows that,without the historical data support,the Case-based Reasoning can still forecast the oil demand accurately.