In response to the shortcomings of low prediction accuracy and large errors in current grain yield prediction methods,a grain yield prediction model DEGWO-RBFNN was proposed,in which the differential evolution adaptive grey wolf algorithm was integrated to optimize the regularization term radial basis function neural network.To improve the search accuracy of GWO,an exponential distribution random number was introduced to initialize the population and improve the initial population quality.An adaptive scaling factor was designed to balance global search and local development that combined sigmoid function.A differential evolution mechanism was introduced to improve the global search ability.The improved GWO was utilized to search for key parameters of radial basis function neural networks,solving the shortcomings that mesh tuning parameters is easy to fall into local optima and is sensitive to initial parameter values.The results show that compared with GWO-RBFNN,RBFNN,DE-RBFNN,BPNN,GA-BPNN,support vector machine and random forest,the prediction accuracy of DEGWO-RBFNN reaches 96.06％,which can be improved by 2.47％to 14.79％compared to that of the comparative models respectively.
关键词
径向基神经网络/粮食产量预测/灰狼优化算法/差分进化/指数分布/自适应缩放因子/正则项
Key words
radial basis function neural network/grain yield prediction/grey wolf optimization algorithm/differential evolution/exponential distribution/adaptive scaling factor/regular term
维普
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