现代信息科技2024，Vol.8Issue(9)：153-157,5.DOI:10.19850/j.cnki.2096-4706.2024.09.032
基于COPSO-GRNN的土壤重金属含量预测模型
Prediction Model for Soil Heavy Metal Content Based on COPSO-GRNN
摘要
Abstract
The prediction of soil heavy metal content is an important part of soil pollution control.To improve the accuracy of prediction,this paper proposes a prediction model for soil heavy metal content based on COPSO-GRNN.In response to the problem that it has difficulty in determining the smoothing factor of generalized regression neural networks(GRNN),the model uses cosine optimization particle swarm optimization(COPSO)for optimization.In addition to adding a small population comparison strategy to the population,it also uses cosine acceleration coefficient to expand the search range and avoid falling into local optima during the optimization process.Then,an adaptation criterion is introduced to improve the convergence speed of the algorithm.Comparative experiments are conducted between this model and several common prediction models for soil heavy metal content.The experimental results show that the predicted values of this model are closer to the true values and has better predictive performance.关键词
土壤重金属含量预测/广义回归神经网络/余弦优化粒子群算法/参数优化Key words
the prediction of soil heavy metal content/GRNN/COPSO/parameter optimization分类
信息技术与安全科学引用本文复制引用
曹文琪..基于COPSO-GRNN的土壤重金属含量预测模型[J].现代信息科技,2024,8(9):153-157,5.基金项目
武昌工学院校级科研项目一般项目(2023KY11) （2023KY11）
