摘要
针对长短期记忆网络(LSTM)在粮食产能预测上存在超参数众多、长时序列信息丢失以及难以区分主次特征的问题,提出一种数据驱动的粮食产能组合预测模型.在超参数部分,通过引入动态权重和拉普拉斯变异的秃鹰算法(WLBES)对LSTM进行超参数寻优,避免了手动调参的过程.在预测部分,利用岭回归(RR)对预测结果进行残差修正,弥补LSTM数据丢失的缺陷;同时加入注意力机制,以权重大小区分主次特征,提升粮食产能相关性较大特征的重要性.研究结果表明,WLBES-LSTM-RR组合模型与LSTM模型和WLBES-LSTM模型相比,均方根误差(RMSE)分别下降了75%、19%,相较于其他优化LSTM的组合模型,RMSE大幅下降,该组合模型在粮食产能预测上具有更高的预测精度.
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 capacity 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 Dynamic 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,respectively,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.
作者
张岳
陈为真
陈梦娇
ZHANG Yue;CHEN Weizhen;CHEN Mengjiao(School of Electrical and Electronic Engineering,Wuhan Polytechnic University,Wuhan 430023,China)
出处
《南京信息工程大学学报（自然科学版）》
CAS
北大核心
2024年第1期46-55,共10页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
湖北省教育厅科技项目(B2020061)。
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
grain production capacity forecast
bald eagle search optimization algorithm
long short-term memory(LSTM)
Laplacian variation
attentional mechanism
residual correction
作者简介
张岳,男,硕士生,研究方向为智能预测建模及应用.980224622@qq.com;通信作者:陈为真,女,博士,副教授,研究方向为智能预测建模及应用、智能计算与决策.38306783@qq.com。
