An Early Warning Mechanism Of China's Banking System's Stability Based On RBF Neural Network

I used empirical analysis and the normative analysis unifies as well as the comparison and the logic reasoning research methods in this paper, and embarked from the difficulty of building of early warning mechanisms of China's banking system. Then I chose the artificial neural network theory as an early warning model, selected 16 parameters for indicator system, and then established the early warning mechanism of the banking system's stability in China based on the artificial neural network. Then I made an empirical study on the stability of China's banking system, predicted the stability of China's banking system in 2006 and 2007, analyzed the reasons for the forecast results, the decline of the stability of China's banking system for recent years, and put forward relevant policy recommendations.The mainly innovation in my paper is the building of early warning indicator system of stability of China's banking system, and the use of SPSS software, the factor analytic method to make a comprehensive and objective evaluation of the stability of China's banking system, and then got the synthesis score concerning the stability of the bank system, for longer-term warning, which is targeted at the shortcomings that the early warning mechanism that China's commercial banks have generally used is unable to quantify the situation, the information can only be used to target hindsight, and the valid period of the outcome of the rating period is too short, and for the sake of the characteristic that the artificial neural networks theory may approach the random nonlinear.The research mentality and the method used in this paper is uncommon in the current academic circles, and as further in-depth study, I believe that the early warning model of the stability of the banking system based on artificial neural network will be constantly improved, and ultimately put into practice.