Credit evaluation model and applications based on probabilistic neural network

Abstract

The article introduces the method of probabilistic neural network (PNN) and its classifying principle. It constructs a PNN structure for identified three patterns samples. The PNN structure is used to separate 106 listed companies of our country in 2000 into three groups. The simulations show that, the classification accuracy rate of PNN to the training samples is very high which is up to 100%, but the classification accuracy rate of PNN to the testing samples is very low which is only 61.11%. Therefore, the classification effect to the population tends to bad and the accuracy rate is only 85.42%. Therefore, PNN is not suitable to identify a new sample. But comparing with Yang's work about PNN's classification (the classification accuracy rate is 74%) effect, the classification effect of the PNN structure given by here is better. Therefore, as a discussion of method, PNN still have research value.