基于支持向量机的备件需求预测研究

针对传统以统计学为基础的预测方法难以解决小样本预测精度不高的实际问题,将支持向量机回归原理应用到备件需求预测领域,构建基于支持向机备件需求预测模型,以及需求预测结果准确率的评价指标。以实际数据为例,分别运用了指数平滑法、网格搜索法优化参数的支持向量机和遗传算法优化参数的支持向量机进对重点备件的需求量进行预测,验证了遗传算法优化的支持向量机预测性能的先进性。结果证明将支持向量机理论应用到备件保障领域具有重要的实用价值。

Optimization of Two-echelon Joint Inventory Based on Allocation Schemes

Focusing on the practical problem of low precision of the conventional prediction method,the actual data on aerial support vector machine classification of spare models are applied to verify the superiority of their classification. The exponential smoothing method,grid search method optimization parameters of support vector machines and genetic algorithm to optimize parameters of support vector machines are respectively used to forecast key aerial spare demand. The result shows that the genetic algorithm optimization of support vector machine forecasting performance is the best. Results prove that the support vector machine theory is applied to the field of aerail spare security has important practical significance.