软件导刊2024，Vol.23Issue(12)：75-81,7.DOI:10.11907/rjdk.241871
粒子群优化的随机森林算法在二次润叶参数寻优中的研究
Research on Optimization of Secondary Leaf Watering Parameters by Particle Swarm Optimized Random Forest Algorithm
摘要
Abstract
The moisture content and temperature stability at the exit of the secondary moistening leaves are the key indexes to evaluate the re-curing process of tobacco leaves.However,it is difficult to accurately control the outlet index of secondary moistening in a regrilling plant in Yunnan province due to parameters such as ambient temperature and water steam flow.Through the construction of random forest algorithm model based on particle swarm optimization,the influence of various parameters on the export index of two rungs under different working condi-tions was explored.After cleaning the historical data of secondary leaf wetting parameters,Pearson coefficient analysis was carried out after re-moving dirty data to find the key production control parameters closely related to export quality.Combined with field manual experience and correlation analysis,the random forest algorithm of particle swarm optimization was used to optimize the return air temperature,hot air temper-ature,drain damper and compensation steam valve opening,and compared with random forest,gray wolf optimization random forest and BP neural network.The results show that the mean square error of return air temperature and hot air temperature obtained by the proposed algo-rithm is 0.003,and the mean square error of the opening of the tidal damper and the compensating steam valve is 0.001.The algorithm pro-vides a theoretical basis for operators to adjust the equipment and improve the quality of tobacco recuring.关键词
二次润叶/随机森林/粒子群优化/关联分析/均方误差Key words
secondary leaf rinsing/random forest/particle swarm optimization/correlation analysis/mean square error分类
信息技术与安全科学引用本文复制引用
朱毓航,李俊,李继斌,李晓冬,毛林伟,杨博,张达富,罗晓峰..粒子群优化的随机森林算法在二次润叶参数寻优中的研究[J].软件导刊,2024,23(12):75-81,7.基金项目
云南省重大科技专项计划子课题(202202AD080006) （202202AD080006）
