Credit evaluation method for optimizing generalized regression neural network based on grey wolf algorithm

Abstract

The invention relates to the technical field of risk control of the Internet financial industry, in particular to a credit evaluation method for optimizing a generalized regression neural network based on a grey wolf algorithm. The method comprises six steps, and compared with common BP and RBF neural networks, the method has the advantages that GRNN selected by the method is strong in nonlinear mapping capability, good in approximation performance and suitable for processing unstable data. The method has the advantages of being good in generalization ability, high in fitting ability, high intraining speed, convenient in parameter adjustment and the like, and compared with common optimization algorithms such as genetic algorithms and particle swarms, the grey wolf algorithm is few in parameter and simple in programming, and has the advantages of being high in convergence speed, high in global optimization ability, potential in parallelism, easy to implement and the like. The grey wolfalgorithm is adopted to optimize the GRNN network model, the prediction precision and stability are high, the defects that the GRNN prediction result is unstable and is very likely to fall into the local minimum value are effectively avoided, and rapid and accurate online real-time prediction of the credit score of the application user is achieved.

Description

technical field [0001] The invention relates to the technical field of risk control in the Internet financial industry, and specifically relates to a credit evaluation method based on a gray wolf algorithm to optimize a generalized regression neural network. Background technique [0002] In terms of evaluation methods, the massive data and fast response requirements of Internet finance make artificial intelligence methods the best choice, and machine learning algorithms such as logistic regression, decision trees, support vector machines, and Bayesian networks have been successfully applied in various empirical studies . With the rapid development of artificial intelligence technology, neural networks such as error backpropagation (BP), radial basis function (RBF), and self-organizing map (SOM) have become important research fields for Internet financial credit evaluation. However, BP and RBF neural networks have the disadvantages of slow learning speed, easy to fall into l...

AI Technical Summary

Problems solved by technology

The prior art uses Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize the smoothing factor σ, but the Genetic Algorithm has disadvantages such as complex operations such as encoding, decoding, crossover, and mutation, and long training time; In the later stage of the process, it is easy to fall into the local extremum area, and there are problems such as slow convergence speed.

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