Study on Credit Scoring Model and Forecasting Based on Probabilistic Neural Network
概率神经网络信用评价模型及预警研究

The article introduces the method of probabilistic neural network (PNN) and its classifying principle. It constructs a PNN structure for identified two patterns samples. The PNN structure is used to separate 106 listed companies of our country in 2000 into two 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 69.77%. Therefore, the classification effect to the population tends to bad and the accuracy rate is only 87.74%. Further simulating results show the predicting accuracy rate is only 69.23% when the PNN is used to predict 13 pre-distressed companies which are published in advance from China in 2001. Therefore, PNN is not suitable to identify a new sample or to carry out predicting study. The research also shows that, PNN is not as good as MLP (to the same data, the classification accuracy rate of the multilayer perceptron is 98.11%). But compare 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.