首页|基于经验模态分解与极限学习机的粮食产量模型预测

基于经验模态分解与极限学习机的粮食产量模型预测

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
由于粮食产量中的历史数据存在较强的时间序列非平稳性和复杂性,传统的单一极限学习机(Extreme Learning Machine,ELM)模型具有低预测精度和差鲁棒性的问题.本文通过优化鲸鱼优化算法(Whale Optimization Algorithm,WOA)的内部参数,将分解后的分量模型预测结果进行叠加,使得对粮食产量的预测更加精准.首先在建立预测模型之前引入经验模态分解模型从原始数据中提取内在特征;其次根据分解得到多个平稳的粮食模态分量,并建立预测模型.实验结果表明,提出的EMD-ELM-WOA组合预测模型与单一的ELM神经网络、BP神经网络、SVM模型、EMD-ELM模型相比预测误差最小,精度最高.
Prediction of Grain Yield Model Based on Empirical Mode Decomposition and Extreme Learning Machine
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.

empirical mode decompositionextreme learning machinegrain production predictionsignal processingfeature extraction

袁世一

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中国农业科学院农业信息研究所,北京 100081

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

国家自然科学基金中国农科院农业信息研究所基本科研业务费项目中国农科院农业信息研究所基本科研业务费项目

62103418JBYW-AII-2022-08JBYW-AII-2022-38

2024

10.3969/j.issn.1006-2475.2024.03.008

计算机与现代化
江西省计算机学会 江西省计算技术研究所

计算机与现代化

CSTPCD
影响因子：0.472
ISSN：1006-2475
年,卷(期)：2024.(3)
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