Based on time series and RBF network plant disease forecasting

Abstract The RBF network is one novel effective forward neural network, it realizes through the nonlinear primary function's linear combination from space RN to the space RM nonlinear transformation, BFm and GM (1,1) combined model,especially qualify in nonlinear time series plant disease forecast. Model GM(1,1) was built for plant disease collected during simulative occurrence experiments. At the same time, the data of disease index and disease incidence were analyzed as reference.The results of experiments reveal that the co-deviation of Model GM(1,1) parameter a and b, cov(a,b), coincides with the standard deviation of disease index and disease incidence. This indicates that Grey system theory is effective for disease incidence analysis and the parameters of GM(1,1) can well reflect the change of plant disease occurrence.This article takes the forecast object by the wheat banded sclerotial blight, proposed that based on the RBF network's plant disease forecasting model, the simulation experiment showed that this model to the plant disease short and medium term forecast is possible effective.