南京信息工程大学学报2024，Vol.16Issue(1)：46-55,10.DOI:10.13878/j.cnki.jnuist.20230424001
数据驱动的粮食产能组合预测模型
Data-driven grain productivity forecasting model
摘要
Abstract
To address the problem of numerous hyperparameters,loss of long time series information and difficulty in distinguishing primary and secondary features in Long Short-Term Memory network(LSTM)for grain yield ca-pacity prediction,this paper proposes a combined data-driven grain capacity forecasting model.In the hyperparameter part,the proposed model performs hyperparameter search optimization for LSTM by introducing Dy-namic Weights and Laplacian variation of Bald Eagle Search Optimization Algorithm(WLBES),to avoid the process of manual parameter adjustment.In the prediction part,the proposed model uses Ridge Regression(RR)to correct the residuals of the prediction results to make up for the deficiency of LSTM data loss,and adds an attention mechanism to distinguish primary and secondary features by weight size to enhance the importance of features with greater relevance to grain production.The results show that the combined WLBES-LSTM-RR model decreases the root mean square error(RMSE)by 75%and 19%compared with the LSTM and WLBES-LSTM models,respective-ly,and substantially decreases the RMSE compared with other combined models of optimized LSTM.This combined model has higher prediction accuracy in grain yield capacity prediction.关键词
粮食产能预测/秃鹰优化算法/长短期记忆网络/拉普拉斯变异/注意力机制/残差修正Key words
grain production capacity forecast/bald eagle search optimization algorithm/long short-term memory(LSTM)/Laplacian variation/attentional mechanism/residual correction分类
信息技术与安全科学引用本文复制引用
张岳,陈为真,陈梦娇..数据驱动的粮食产能组合预测模型[J].南京信息工程大学学报,2024,16(1):46-55,10.基金项目
湖北省教育厅科技项目(B2020061) （B2020061）
