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In order to study the early warning of companies' financial risk, this paper used two models based on factor analysis, which are logistic regression and BP neural network. Finally, for the warming accuracy, BP neural network model is better than logistic regression model.
The article develops a BP network for trip chaining pattern recognition based on the data obtained from Beijing Resident Trip Survey. First a set of socioeconomic and demographic factors related to traveller information which potentially influence trip-chaining patterns are pre-treated through principle components analysis, therefore seven variables are selected as input variables of neural network,...
The prediction accuracy of grey theory was limited by it’s high requirement of data’s smoothness. BP neural is adept in solving nonlinear problem and performs well in self-adaption and self organization, but it’s training effect and efficiency was limited by the number of data. A hybrid model combined advantages of grey theory and BP neural network is put forward based on analysis of gyro motor’s...
The traditional stationary network traffic model (ARIMA) is incapable of describing non-stationary characteristics. In the process of predicting, the accuracy will weaken with the increase of step. As a non-stationary network traffic model, NN (neural network) could make up for the defect of stationary model, which can not describe the non-stationary qualities of the network traffic. However, the...
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