安徽农业科学2026，Vol.54Issue(2)：21-25,32,6.DOI:10.3969/j.issn.0517-6611.2026.02.003
基于IPSO-BP模型的牧草产量预测方法研究
Research on Forage Yield Prediction Method Based on IPSO-BP Model
摘要
Abstract
To address the multi-factor coupling challenges in forage yield prediction,this study focuses on the Shuozhou municipal area as the research subject.By integrating data(including temperature,precipitation,sunshine duration,and 10 cm ground temperature data)from six national basic meteorological stations from 2005 to 2022,and coupling with the region's annual forage yield data and growth-stage climate re-quirement characteristics over the past decade,established a BP neural network-based foundational prediction model.The model parameters were optimized through the introduction of standard particle swarm optimization(PSO)and improved particle swarm optimization(IPSO)algo-rithms.Simulation comparative studies demonstrate that the IPSO-optimized BP neural network achieves significant performance enhancement in prediction accuracy,exhibiting lower Mean Absolute Error compared with both the basic BP model and PSO-BP model.The algorithm-im-proved model also demonstrates stronger generalization capabilities,with IPSO showing higher global optimization efficiency than basic PSO.These findings validate the application value of the improved algorithm for forage yield prediction under complex meteorological conditions.关键词
牧草产量/预测模型/神经网络/气候条件/微粒群算法Key words
Forage yield/Prediction model/Neural network/Climatic conditions/Particle swarm algorithm分类
管理科学引用本文复制引用
朱彩芬,赵钰,田粉平,马尚谦,张丽丽,刘瑞兰,王高芳..基于IPSO-BP模型的牧草产量预测方法研究[J].安徽农业科学,2026,54(2):21-25,32,6.基金项目
山西省气象局面上项目(SXKMSQH20256308). （SXKMSQH20256308）
