ABSTRACT
Although traditional grey models are easy to understand and simple to calculate, with satisfactory accuracy, but it also lack flexibility to adjust the model to acquire higher forecasting precision. Literatures show performance of traditional grey models still could be improved. The nonlinear grey Bernoulli model NGBM(1,1) proposed by Chen et al. (2008) is a nonlinear differential equation with power index γ . It is a simple modification of GM(1,1) combining with Bernoulli differential equation. By adjusting power exponent, the curvature of the solution curve could be adjusted to fit the result of accumulated generating operation of raw data. To further improve the fitness of NGBM model, many researchers have tried to improve the prediction accuracy of the grey forecasting model by optimizing the selection of model
parameters and power exponent (Chen et al., 2010; Hsu, 2010).
