Research On Eterprise Financial Early Warning Based On HSDM-BP Model

In order to improve the accuracy of the enterprise financial crisis warning effectively, and at the same time aimed at the defects existing in the learning process of the error back propagation neural network, such that it has slow convergence speed and fall into local minimal value easily, etc, puts forward a model based on harmony search combined with differentia] evolution of BP network optimization algorithm to warn the enterprise financial crisis. The variation mechanism of the differential evolution algorithm is used to improve the harmony search algorithm data processing operations so that the search performance of harmony search could be improved; then use the harmony search improved by differential evolution algorithm to complete training after its weights and thresholds of BP were optimized, and get a HSDM-BP model. Take advantage of the trained model for the same amount through ST special treatment or healthy of financial data for training analysis and early warning of listed companies, and the results will be compared with other algorithms. Experiments show that using the model algorithm of this paper to predict the results of the enterprise financial crisis are better than other model algorithms on the prediction accuracy of enterprise crisis divide. The results show that in this paper HSDM-BP network algorithm proposed not only overcome the shortcomings of BP network, improves the accuracy of the financial early warning, while compared with other early warning method is superior in performance.