Research of Financial Crisis Early-Warning System of Listed Company
Wang Guang-zheng · Computer Technology and Development · 2008
Enterprise's financial crisis predicts is the non-linear prediction,there is a complicated association decision relation between each influence factor,and the data in reality are continuous,it is very difficult to be used in the categorized machine to study directly.After analyzing the characteristic of the early warning problem,merged many kinds of soft computing methods to construct the prediction model.Firstly,take consistency level of decision,average information entropy and degree of discretization as evaluation criteria of the result of discretization.Then utilize the overall search of genetic algorithm to find the optimized cut points.After discretization by the optimized cut points,train the BP neural network with the samples in training set.When finishing the training,use the BP neural network to predict financial crisis of listed company,and the experimental result indicates,the prediction rate is up to 93%.