A proposed GM-GRNN model for prediction of behavior in complex system

This paper analyses the kernel of the general regression neural network (GRNN) model in detail, and presents its deficiencies in the domain of complex systems forecasting. We import various aspects of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method and GM(1,h) algorithms to improve the kernel of the GRNN model. We then apply this modified model to the problem of unemployment forecasting in China, as an example of its ability to model time-varying environments.