Grain Production Prediction Based on Grey-Relational Support Vector Machine

In order to improve the grain yield prediction accuracy,this paper presented a grain output prediction model of grey support vector machine.First,grey correlation analysis was used to identify the main factors affecting the changes in grain output,and then by support vector machine,the nonlinear mapping relation between grain crop and factors was built.Finally,to avoid the blindness of artificial selecting parameters,a genetic algorithm was used to determine the parameters of support vector machine and the future of grain output prediction.The simulation experiments are carried out using the grain crops of China from 1978 to 2011.The predicted results were,compared with a single machine model,and the results show that,the grey support vector machine can increase the prediction accuracy of grain output.