Department of Logistics Management, Xi'an International University, Xi'an 710077, China.
Comput Intell Neurosci. 2022 Sep 24;2022:1591371. doi: 10.1155/2022/1591371. eCollection 2022.
In the context of rapid social development, a logistics financial model that can meet the financing needs of small and medium-sized enterprises and has high returns is widely used in all aspects of the logistics financial industry. Logistics finance is a new financing model that can effectively integrate logistics enterprises, financial companies, and financing institutions to achieve mutual benefit and win-win results. The uncertainty of financial information, the motivation of each business service object to pursue high returns in a short period of time, and the inadequate risk preuniversal conditions have led to credit risks in the development of logistics financial services. Promoting the close integration of improved neural network algorithms based on machine learning and logistics financial financing models is inseparable from the active cooperation of all aspects, the trust of various business service objects, and the construction of logistics financial information platforms. Based on machine learning, this paper analyzes and models the collaborative development of logistics finance, analyzes the original data, and constructs sample characteristics. Due to the small amount of information in part of the sample features, this causes problems such as overfitting in the process of model building. Therefore, we designed a new feature selection based on Pearson correlation coefficient and PCA. . Using this algorithm for feature selection, an integrated learning method is proposed. In order to solve the shortcomings of traditional neural network logistics algorithms, a neural network-based noncomplete vehicle path optimization mining model is proposed. By weighting the time domain length and spatial probability of logistics finance, the stable state of the neural network is restricted. Simulation results show that this method can effectively improve logistics efficiency and maximize the economic value of the transportation process.
在快速社会发展的背景下，一种能够满足中小企业融资需求且回报率高的物流金融模式在物流金融行业的各个方面得到了广泛应用。物流金融是一种新的融资模式，可以有效地整合物流企业、金融公司和融资机构，实现互利共赢的结果。金融信息的不确定性、每个商业服务对象在短时间内追求高回报的动机，以及物流金融服务发展中风险防范条件的不足，导致了信用风险。推动基于机器学习的改进神经网络算法与物流金融融资模式的紧密结合，离不开各方面的积极合作、各商业服务对象的信任以及物流金融信息平台的建设。本文基于机器学习，对物流金融的协同发展进行了分析和建模，对原始数据进行了分析，并构建了样本特征。由于部分样本特征的信息量较小，这在模型构建过程中导致了过拟合等问题。因此，我们设计了一种新的基于 Pearson 相关系数和 PCA 的特征选择。利用这种算法进行特征选择，提出了一种集成学习方法。为了解决传统神经网络物流算法的缺点，提出了一种基于神经网络的非满载车辆路径优化挖掘模型。通过对物流金融的时域长度和空间概率进行加权，限制了神经网络的稳定状态。仿真结果表明，该方法可以有效地提高物流效率，最大限度地提高运输过程的经济价值。
