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黑龙江大学电子工程学院
Published:2021
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[1]顾丽丽,刘勇,甄佳奇.基于改进粒子群算法优化BP神经网络的甜菜产量预测方法[J].新疆大学学报(自然科学版)(中英文),2021,38(02):191-196.
[1]顾丽丽,刘勇,甄佳奇.基于改进粒子群算法优化BP神经网络的甜菜产量预测方法[J].新疆大学学报(自然科学版)(中英文),2021,38(02):191-196. DOI: 10.13568/j.cnki.651094.651316.2020.04.06.0001.
DOI:10.13568/j.cnki.651094.651316.2020.04.06.0001.
通过分析影响甜菜产量的自然因素
选取6个主要影响因子应用于一种改进粒子群算法优化BP神经网络的预测模型.首先
在标准粒子群算法(Particle Swarm Optimization
PSO)中引入自适应惯性权重的方法增强搜索能力并且提高收敛速度
使用反向逃逸策略避免早熟现象的发生;将改进的粒子群算法引入到BP中形成NCPSO-BP的预测模型算法
既缩短了运算时间
又提高了预测精度;最后将NCPSO-BP与PSO-BP的预测效果进行对比
结果表明NCPSO-BP预测模型其最优预测结果的相对误差平均值3.59%
绝对误差平均值0.196 9
比PSO-BP模型预测误差有所下降.通过这次智慧农业实验项目的应用
实现当年甜菜产量增产50%
为未来推广到面积更大、机械化程度更高的农田应用打下了基础
对现代化农业具有一定意义.
By analyzing the natural factors which affect beet yield
select six main influence factors and apply to an improved particle swarm algorithm to optimize the prediction model of BP neural network.First
an adaptive inertial weighting method is introduced in the standard particle swarm optimization(Particle Swarm Optimization
PSO)to enhance search capabilities and improve convergence speed;Use reverse escape strategy to avoid precocity.The improved particle swarm optimization algorithm is introduced into BP to form the prediction model algorithm of NCPSO-BP
the algorithm shortens the operation time and improves the prediction accuracy. Finally
the prediction effects of NCPSO-BP and PSO-BP are compared
the results show that relative error mean of the NCPSO-BP prediction model is 3.59% and mean absolute error is 0.196 9
which is lower than that of the PSOBP model.Through this intelligent agricultural experiment project
a 50% increase in sugar beet production was achieved that year
laying a foundation for future extension to larger areas and deeper mechanized farmland
which has certain significance for modern agriculture.
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