舰船运输与补给需求预测问题研究

Peng Dong, Peng Yu, Furong Qin
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引用次数: 0

摘要

针对需求预测问题,采用基于案例推理(case-based reasoning, CBR)方法生成样本,采用最小二乘支持向量机(least squares support vector machines, LSSVM)模型作为预测模型,对海岛进攻作战防空弹药需求预测进行实证分析。结果表明,基于案例推理生成的样本是可用的,不同模型的预测结果一致且更准确,该方法解决了样本不足的问题,适用于作战物资需求预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Transportation and Replenishment Demand Forecasting Problem of Naval Warship
To solve the problems of demand forecasting, the case-based reasoning (CBR) is used to create samples, and the least squares support vector machines (LSSVM) model is used as forecasting model, then the anti-air ammo demand forecasting in island offensive operation is taken as an empirical analysis. The result indicates that the samples created by case-based reasoning are available, and the forecasting results of different models are consilient but more accurate, this method can solve the problem of the lack of samples, so it is applicable to the combat supplies demand forecasting.
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