计算机与现代化Issue(3)：47-53,7.DOI:10.3969/j.issn.1006-2475.2024.03.008
基于经验模态分解与极限学习机的粮食产量模型预测
Prediction of Grain Yield Model Based on Empirical Mode Decomposition and Extreme Learning Machine
摘要
Abstract
Due to the strong time series non-stationarity and complexity in historical data of grain production,the traditional single Extreme Learning Machine(ELM)models suffer from low prediction accuracy and poor robustness.This paper optimizes the internal parameters of Whale Optimization Algorithm(WOA)and superimpose the predicted results of decomposed compo-nents model to achieve more accurate predictions of grain production.Firstly,the Empirical Mode Decomposition(EMD)model is introduced to extract intrinsic features from raw data before establishing the prediction model.Secondly,the multiple stationary grain mode components are obtained by decomposition,and a prediction model is established for each component.The experi-mental results show that the proposed EMD-ELM-WOA combined prediction model outperforms single ELM neural network,BP neural network,SVM model,and EMD-ELM model with minimal prediction error and highest accuracy.关键词
经验模态分解/极限学习机/粮食产量预测/信号处理/特征提取Key words
empirical mode decomposition/extreme learning machine/grain production prediction/signal processing/feature extraction分类
信息技术与安全科学引用本文复制引用
袁世一..基于经验模态分解与极限学习机的粮食产量模型预测[J].计算机与现代化,2024,(3):47-53,7.基金项目
国家自然科学基金资助项目(62103418) （62103418）
中国农业科学院农业信息研究所基本科研业务费项目(JBYW-AII-2022-08,JBYW-AII-2022-38) （JBYW-AII-2022-08,JBYW-AII-2022-38）
