Research on the Nonlinear Simulation Early-Warning System of the Local Government Debt Risk——Based on Rough Sets and BP Neural Network Integration
Xing Liu · Journal of Shanxi Finance and Economics University · 2012
Firstly,the paper designs the early-warning indicator system of the local government debt risk.Then,the paper integrates rough sets and BP neural network to build the nonlinear simulation early-warning system of the local government debt risk.Selecting china's eastern,central and western regions 27 samples to carry out the empirical research in the period 2007-2009,the results show that Most of the debt risks in sample regions show the state of middle warning degree or above the state,the local government debt risk is totally high.Meanwhile,in the period 2007 to 2009,the comprehensive evaluation of the risk in all sample regions is rising,which also illustrates the local government debt risk in our country are showing a rising trend in recent years.Regarding the simulation effect,compared to the system of simple BP neural network,the RS-BP neural network system not only reduces the complexity of BP neural network,saving training time,but also has better early-warning accuracy and application value.