Study of prediction model on grey relational BP neural network based on rough set

Artificial neural network is a type of large-scale nonlinear dynamical system capable of recognizing the obscure relationships between diverse variables. Its redundant input nodes often POST http://www.icmlc.org/Author/Author_Rts. With the introduction of rough set and grey relation theories, condition attributes were considered as correlation sequences and decision attributes as reference sequences. And the grey correlation coefficient represented the weight upon which the condition attributes were reduced and the initial decision table was renewed with the remaining core factors. As a result of training the BP neural network by the reduced condition attributes, the prediction precision was improved prominently. In the application of this model to forecast the grain yields of China in 2001 and 2002, the results show great improvement of prediction precision as 0.83% and 1.93% respectively. And the fitting precision of the grain yields in the other 11 years (1990-2000) are all above 99%. The redundancy elimination also increases the network training rate by reducing the input and hidden nodes.