基于二阶振荡微粒群最小二乘支持向量机的物流需求预测

为了提高物流需求的预测精度,在分析物流需求影响因素基础上,建立了物流需求的二阶振荡微粒群最小二乘支持向量机预测模型。利用最小二乘支持向量机(LSSVM)描述物流需求与其影响因素间的复杂非线性关系,并通过二阶振荡微粒群(TOOPSO)算法优化选择LSSVM参数。实例分析表明,模型具有较高的预测精度,TOOPSO算法搜索LSSVM最优参数时间明显少于传统交叉验证法,是一种有效的物流需求预测方法。

Logistics demand forecasting based on LSSVM optimized bytwo-order oscillating PSO

Based on analyzing the factors of logistics demand, this paper proposed a new model named the two-order oscillating particle swarm least squares support vector machines TOOPSO-LSSVM model to improve the forecasting accuracy of logistics demand. The complex nonlinear relationship between logistics demand and its impact factors were explained through LSSVM. And then, it used TOOPSO algorithm to optimize the parameters of LSSVM model. An empirical analysis indicates that the forecasting performance of LSSVM is better than the other three models and the searching time for optimal parameters of LSSVM by TOOPSO is obviously less than cross validation method, which is an effective method for logistics demand forecasting.