首页|Ningbo Polytechnic Researcher Describes Advances in Support Vector Machines (Ris k Warning Method of Corn Cross-border Supply Chain Based on DBN-MFSVM)
Ningbo Polytechnic Researcher Describes Advances in Support Vector Machines (Ris k Warning Method of Corn Cross-border Supply Chain Based on DBN-MFSVM)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on have been presented. According t o news reporting out of Zhejiang, People’s Republic of China, by NewsRx editors, research stated, “There is a large amount of unstructured data in the current c orn cross-border supply chain system and it has the characteristics of multi-sou rce heterogeneous.” Our news journalists obtained a quote from the research from Ningbo Polytechnic: “Traditional risk early warning methods have defects such as over-reliance on m anual decision-making and low accuracy of early warning. In order to solve the a bove problems, this paper proposed a system risk early warning method of corn cr oss-border supply chain based on deep belief network and multi-class fuzzy suppo rt vector machine. Firstly, based on the principle of embedding coding and norma lization, a large number of unstructured data in the corn cross-border supply ch ain system were preprocessed and converted into structured data for subsequent c alculation. Then, based on the deep belief network, the high-latitude features o f the data were extracted, and the change trend and correlation of risk indicato rs in the corn cross-border supply chain system were adaptively mined. Finally, the extracted high-dimensional features were input into the multi-class fuzzy su pport vector machine model for training to realize the risk classification early warning of corn cross-border supply chain. The accuracy of the algorithm propos ed in this paper can reach 94.88% under the condition of similar r unning time.”
Ningbo PolytechnicZhejiangPeople’s R epublic of ChinaAsiaEmerging TechnologiesFuzzy LogicMachine LearningSu pport Vector MachinesVector Machines
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