Abstract
The genetic BP algorithm is used to modify and optimize the connection weights and thresholds of the neural network, which solves the problem that BP neural network has slow convergence speed and may fall into local minimum to a certain extent. The accuracy of rating indicates that the genetic neural network method is very suitable for enterprise credit rating.
Highlights
IntroductionEnterprise credit rating can provide fair credit information for economic management departments, financial institutions and investors to avoid risks, optimize investment and improve efficiency
Enterprise credit rating can provide fair credit information for economic management departments, financial institutions and investors to avoid risks, optimize investment and improve efficiency.The traditional rating method based on statistics, expert system method and artificial neural network method
BP Neural network provides a new method for credit rating
Summary
Enterprise credit rating can provide fair credit information for economic management departments, financial institutions and investors to avoid risks, optimize investment and improve efficiency. The traditional rating method based on statistics, expert system method and artificial neural network method. The idea of these methods is to find out the rules of classification and establish discriminant models from historical data, and use them to classify new samples. BP Neural network provides a new method for credit rating. It is good at learning useful knowledge from input and output data, easy to implement parallel computing. We adopt the method of adaptive learning rate and increasing momentum term (improved BP neural network algorithm) based on the application model of credit rating. When the error between the output and the actual output is less than a certain specified value, the training process stops, and the weights and thresholds are determined to obtain the required network structure to get the rating results
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