大型物流企业财务困境预警模型的建立与仿真研究

Establishment and Simulation of Financial Difficulty Forewarning Model of Large-scale Logistics Enterprises

为了有效提高大型物流企业财务预警准确率,提出一种RBF神经网络的大型物流企业财务困境预警模型(PSO-RBFNN)。首先选取商务部直属的75家大型物流企业中的60家作为研究对象,按照其财务状况将其设置为轻警、重警、巨警3种类别,将选取的物流企业划分为训练集与测试集,然后利用训练集对RBF神经网络进行训练建模,并利用粒子群算法优化RBF神经网络的参数,最后建立RBF神经网络的大型物流企业财务预警模型,并通过仿真实验对其预警性能进行检验。

In this paper, we proposed a financial difficulty forewarning modal of large-scale logistics enterprises based on RBF neural network： first we selected 60 out of the 75 large-scale logistics enterprises directly affiliate to the ministry of commerce as the research object, divided them into the categories of alight warning, serious warning and severe warning according to their financial situation, then used PSO to optimize the parameters of the RBF neural network and at the end established the enterprise financial difficulty forewarning model.