Grey-neural forecasting system

In this paper, a new nonlinear forecasting system using a grey predictor model and neural network tuner is proposed. This paper puts its emphasis on a few data and incomplete information to build the predictive system, excavates the connotative essence of a signal from the GM (1,1) predictor and refers to anomalistic (over predictive error) conditions to build the neural tuner database. So in the forecasting model the GM (1,1) model system predictive value will be appreciably modified while the anomalistic condition occurs. Simulation in well-known Mackey-Glass time series is presented to demonstrate the performance of the proposed predictive system.