摘要
为了提高物流需求预测精度,针对物流需求的复杂变化特性,提出一种蚁群算法(ACO)优化最小二乘支持向量机的(LSSVM)的物流需求预测模型(ACO-LSSVM).首先对物流需求数据进行重构,然后采用LSSVMY刻画物流需求的复杂非线性变化特性,并通过ACO算法优化选择LSSVM参数,采用物流需求预测实例对ACO-LSSVM性能进行测试.结果表明,ACO-LSSVM提高了物流需求预测精度,是一种有效的物流需求预测方法.
In order to improve the forecasting accuracy of logistics demand, this paper puts forward a logistics demand forecasting model based on least support vector machines optimized by ant colony optimization algorithm （ACO- LSSVM）. Firstly, the data of logistics demand are reconstructed, and then the complex nonlinear change rule of logistics demand is explained through LSSVM, and the parameters of LSSVM model are optimized by ACO, and lastly, the performance of mode are tested by logistics demand data. The simulation results show that ACO-LSSVM has improved the forecasting accuracy of logistics demand, and which is an effective method for logistics demand forecasting.
出处
《计算机系统应用》
2013年第5期107-110,共4页
Computer Systems & Applications
关键词
物流需求
蚁群优化算法
最小二乘支持向量机
预测
logistics demand forecasting
ant colony optimization algorithm
least support vector machine
prediction
