| This paper studies annual production of peanut in Shandong province. Considering the problem of difficult prediction and low accuracy due to strong volatility in peanut annual production, this paper proposes a novel combined model on the basis of GM (1,1) model and RBF neural network. GM (1,1) is to capture the global trend of peanut annual production, and RBF neural network is to predict the strong nonlinear residual item. To improve the training velocity and accuracy, considering the precocious phenomenon and slow convergence rate of standard genetic algorithm, a new selfadaptive genetic algorithm is proposed to optimize initial parameters of RBF neural network. Experimental results demonstrate the new combined model can accurately predict the peanut annual production, which shows the feasibility of this combined model. |
