湖南农业大学学报（自然科学版）2017，Vol.43Issue(2)：222-225,4.DOI:10.13331/j.cnki.jhau.2017.02.020
基于PSO–BP算法的油菜籽干燥工艺参数的优化
Parameter optimization of vacuum drying process for rapeseed based on PSO?BP algorithms
摘要
Abstract
Vacuum drying process for rapeseed was optimized by combination of neural network and particle swarm algorithms. BP neural network algorithm was used to establish the three layer network model to forecast the relationship between the drying temperature, initial moisture content, pressure and the average rate of moisture dropping, germination rate of rapeseed. Network weight and threshold of the model was calculate by using the sample data from the experiment. Then, PSO algorithm was used to optimize the initial parameters of the model. It is verified by experiment that the maximum relative error of BP networkmodel was 4.5%, whereas it was 2.93% for PSO–BP network model. The combination of BP neural network and PSO algorithms could decrease the error between the actual value and network simulated value.关键词
油菜籽干燥/粒子群算法/神经网络/工艺优化Key words
rapeseed drying/particle swarm optimization/BP neural network/process optimization/intelligent control分类
信息技术与安全科学引用本文复制引用
朱光耀,谢方平,陈凯乐,代振维..基于PSO–BP算法的油菜籽干燥工艺参数的优化[J].湖南农业大学学报（自然科学版）,2017,43(2):222-225,4.基金项目
湖南省教育厅项目(15C0488) （15C0488）
