Risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model

Abstract

Neural network is used to deal with the nonlinear relationship, usually there is a strong nonlinear relationship between input and output. Through the self-learning of neural network, the weight of data samples is determined after training, and the optimal solution is obtained according to the process steps. In this paper, thea authors analyze the risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model. For logistics companies, it is necessary to determine the ability of logistics companies to engage in logistics finance business, and then to make detailed and accurate grasp of relevant information. The difference between the actual output and the expected output of the training sample is small, so the fitting is completed well, and the parameters of the neural network are further adjusted. The results show that the model has a good ability of learning nonlinear function relations. To sum up, in order to reduce logistics financial risks, we must fully understand the factors that affect logistics financial risks, determine the proportion of risk factors, and then use the fuzzy evaluation method to analyze the financial business risks.