Abstract
Personal credit evaluation is the basic method for the commercial banks to avoid the consumer credit risk. On one hand, the credit behavior of individuals is complex; on the other hand the personal credit assessment system in our country is not sound, assessment methods are mostly objective, therefore, more and better scientific methods for credit risk assessment need to be introduced. This paper proposed a method for personal credit evaluation based on PSO-RBF neural network, which used PSO algorithm to optimize the parameters of RBF neural network, then applied the optimized RBF neural network in the personal credit evaluation. This method combined the global searching ability of PSO algorithm and the high effectiveness of local optimize of RBF together, overcame the unstabitily of PSO algorithm and the drawback of RBF which easily leads to local minimum. The result shows that the personal credit assessment method based on PSO-RBF neural network is highly accurate in classification and prediction, and is suitable in personal credit assessment and prediction.
Highlights
With the rapid development of Chinese economy, the personal credit is being taken more and more attention, which strongly challenges the credit evaluation system in our country
This paper proposed a method for personal credit evaluation based on Particle swarm optimization (PSO)-RBF neural network, which used PSO algorithm to optimize the parameters of RBF neural network, applied the optimized RBF neural network in the personal credit evaluation
The result shows that the personal credit assessment method based on PSO-RBF neural network is highly accurate in classification and prediction, and is suitable in personal credit assessment and prediction
Summary
With the rapid development of Chinese economy, the personal credit is being taken more and more attention, which strongly challenges the credit evaluation system in our country. RBF neural network is a three-layered BP network, which can approximate the continuous function with arbitrary precision It is characterized by single best approximation, no local minimum, less calculation and fast learning, and is widely applied to pattern classification, system identification and functional approximation. PSO-RBF neural network model [5] is applied to many industries, such as water quality assessment [6], short time traffic flow prediction [7], and boiler super-. Study of Personal Credit Evaluation Method Based on PSO-RBF Neural Network Model heat steam temperature control systems identification [8] etc. This paper built personal credit assessment model by using RBF neural network, and chose PSO algorithm to train and optimize the network, built personal credit assessment model based on PSO-RBF neural network, and made a further comparative analysis of it with single RBF neural network, the second chapter of this paper provided the modeling solution; the third chapter processed the sample data; the fourth chapter analyzed the result of simulation
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