支持向量机在装备保障链需求预测中的应用研究

Applied Research of Support Vector Machine on Requirement Prediction of Equipment Support Chain

准确的需求预测是装备保障链敏捷运行的重要条件。针对装备保障链需求预测过程中,需求不确定、样本数量较少的实际情况,采用了一种新的预测方法——支持向量机。该方法基于统计学习理论的原理,较好地解决了小样本、非线性的学习问题。建立了装备保障链需求预测的支持向量机模型。并以某物资的需求预测为例进行实例验证,计算结果表明,这种方法比传统的方法有更好的预测精度。

Exact requirement prediction is an important condition for equipment support chain to work agilely. In allusion to the situation that requirement is uncertain and the number of test samples is fewer in the course of predicting requirement of equipment support chain, a new method, support vector machine, is given. The algorithm is based on statistical theory. It can solve the nonlinear learning problem of fewer samples. A support vector machine model is constructed to predict equipment support chain＇ s requirement. And an example of a certain mate- rial＇ s requirement＇ s prediction is given, whose result shows that the method can bring less error and better predicted precision compared with traditional methods.