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

2020 ◽  
Vol 39 (4) ◽  
pp. 5915-5925
Author(s):  
Wang Aiqun ◽  
He Zicong ◽  
Wang Yilin
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Nonlinear Function ◽  
Financial Risks ◽  
Nonlinear Relationship ◽  
Fuzzy Evaluation ◽  
Good Ability

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.

Credit Risk Assessment of Internet Financial Platforms Based on BP Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 29-45
Author(s):  
Yu Yuan ◽  
Yue Yang
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Credit Risk ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Assessment Model ◽  
Accuracy Rate ◽  
Factors Affecting ◽  
Rating Method ◽  
Online Lending

Aiming at the problem of credit risk, this paper selects key data indicators to establish an index system combining with the factors affecting the credit risk of the platform. Python crawler software was used to obtain relevant data of net lending platforms, and the crawled data of more than 1000 platforms were preprocessed. Ninety-five platforms with complete data were selected to build a BP neural network risk assessment model. The BP neural network model is used to make an empirical analysis of the risks of online lending platforms by using the data obtained, and the evaluation method of this paper is compared with the rating method of online lending sky eye. The empirical results show that the error of BP neural network can be stable at about 0.5, and the accuracy rate of evaluation is as high as 95.45%, which is much higher than the accuracy rate of 44.21% of net loan platform. This paper provides decision support for the credit risk early warning of net loan platform.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

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