In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.

中文翻译：

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基于遗传反向传播神经网络的金融企业信用评价研究

本文提出了一种基于遗传算法的改进神经网络企业信用评级模型。具有自适应和自学习的特点，利用遗传算法对神经网络连接的阈值和权重进行调整和增强。结合遗传算法解决了BP神经网络收敛速度慢、可能陷入局部极小点的潜在问题，令人信服。遗传BP神经网络的混合技术应用于信用评级系统。使用商业银行的数据集，我们的实验评估表明，结合使用 BP 神经网络和遗传算法，该模型在企业信用评级中准确率高，具有较好的应用价值。此外，所提出的模型比经典的 BP 神经网络方法准确度高约 15.9%。