College of Economics, Northwest University of Political Science and Law, Shaanxi, 710000, China.
Math Biosci Eng. 2022 May 27;19(8):7886-7898. doi: 10.3934/mbe.2022368.
In China, farmers' loan difficulties have become a major problem restricting increases in farmers' incomes and the economic development of rural areas. The existing studies of the management and control of farmers' credit risk have mostly been pre-management, which cannot efficiently prevent and reduce the occurrence of farmers' credit risk in time. This paper uses the T-S neural network model to build a farmers' credit risk early warning system so that formal financial institutions can predict the occurrence of and changes in the farmers' credit risks in a timely manner and quickly undertake countermeasures to reduce losses. After training and testing, a model with a higher degree of fit is used to analyze the credit level of farmers in Shaanxi Province from 2016 to 2018. The results demonstrate that the credit level of farmers in this area is continuously improving, in agreement with the actual situation. The results also show that the prediction accuracy of the T-S fuzzy neural network is high, verifying the rationality of the selection of test samples.
在中国，农民贷款难已成为制约农民增收和农村经济发展的一大难题。现有的农户信用风险的管理与控制研究大多属于事前管理，不能及时有效地防范和降低农户信用风险的发生。本文运用 T-S 神经网络模型构建农户信用风险预警系统，使正规金融机构能及时预测农户信用风险的发生和变化，并迅速采取应对措施，减少损失。通过训练和测试，选用拟合度较高的模型，对陕西省 2016 年至 2018 年农户的信用水平进行分析，结果表明该地区农户信用水平不断提高，与实际情况相符。同时，T-S 模糊神经网络的预测准确率较高，验证了测试样本选取的合理性。
