基于DBN-MFSVM的玉米跨境供应链风险预警方法

Risk Warning Method of Corn Cross-border Supply Chain Based on DBN-MFSVM

针对当前玉米跨境供应链系统中存在大量的非结构化数据,具备多源异构特点。传统的风险预警方法存在过度依赖人工决策、预警准确率偏低等缺陷。为解决上述问题,提出基于深度置信网络和多类模糊支持向量机的玉米跨境供应链系统风险预警方法。首先基于嵌入编码与归一化原理,预处理玉米跨境供应链系统中的大量非结构化数据,转化为结构化数据,便于后续计算;然后基于深度置信网络,提取数据高纬度特征,自适应挖掘出玉米跨境供应链系统中风险指标变化趋势与关联性;最后将提取出的高维度特征输入到多类模糊支持向量机模型中进行训练,实现玉米跨境供应链风险分级预警。所提算法能够在运行时间相近的情况下,准确率达到94.88%,较最差算法提升52.17%,综合性能较其他算法优越,能够为玉米跨境供应链系统风险监管应用提供理论支撑。

There is a large amount of unstructured data in the current corn cross-border supply chain system and it has the characteristics of multi-source heterogeneous.Traditional risk early warning methods have defects such as over-reliance on manual decision-making and low accuracy of early warning.In order to solve the above problems,this paper proposed a system risk early warning method of corn cross-border supply chain based on deep belief network and multi-class fuzzy support vector machine.Firstly,based on the principle of embedding coding and normalization,a large number of unstructured data in the corn cross-border supply chain system were preprocessed and converted into structured data for subsequent calculation.Then,based on the deep belief network,the high-latitude features of the data were extracted,and the change trend and correlation of risk indicators in the corn cross-border supply chain system were adaptively mined.Finally,the extracted high-dimensional features were input into the multi-class fuzzy support vector machine model for training to realize the risk classification early warning of corn cross-border supply chain.The accuracy of the algorithm proposed in this paper can reach 94.88% under the condition of similar running time.It is 52.17% higher than that of the worst algorithm,and the comprehensive performance was superior to other algorithms which can provide theoretical support for the application of system risk regulation of corn cross-border supply chain.