Statistical optimization of supply chain financial credit based on deep learning and fuzzy algorithm

Issue title: Cross-domain Applications of Fuzzy Logic and Machine Learning

Guest editors: Ekaterina Isaeva and Álvaro Rocha

Article type: Research Article

Authors: Hu, Zijiang^{; *}

Affiliations: Nanjing University of Finance and Economics, Jiangsu, China

Correspondence: [*] Corresponding author. Zijiang Hu, Nanjing University of Finance and Economics, Jiangsu, 210023, China E-mail: hzj_nj0918@126.com.

Abstract: By controlling the transaction background and data of supply chain enterprises, supply chain finance can reduce the degree of information asymmetry in the process of enterprise financing and provide more financing mode options for enterprises. In this paper, the author analyzes the statistical optimization of supply chain financial credit based on deep learning and fuzzy algorithm. We use particle swarm optimization to train BP neural network and improve the previous algorithm. By changing the speed of the particle search in the weight space, that is, updating the weight of the net-work, the mean square error of the network output is gradually reduced. Simulation results show that the model is helpful to analyze the correlation between supply chain finance and economy, compared with the traditional BP neural network, the original data of BP neural network based on particle swarm optimization is better fitted, so it can be used to predict supply chain financial credit level.

Keywords: Deep learning algorithm, supply chain finance, credit rating, fuzzy algorithm

DOI: 10.3233/JIFS-179796

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 6, pp. 7191-7202, 2020