QPSO-optimized BP Neural Net ork to Predict Occurrence Quantity of Myzus persicae

编号 zgly0001495651

文献类型 期刊论文

文献题名 QPSO-optimized BP Neural Net ork to Predict Occurrence Quantity of Myzus persicae

作者 Qiu Jing  Yang Yi  Qin Xiyun  Li Kunlin  Chen Keping  Yin Jianli 

作者单位 College of Foundation and Information Engineering  Yunnan Agriculture University  Yunnan Tobacco Research Institute 

母体文献 Plant Diseases and Pests 

年卷期 2015年01期

年份 2015 

分类号 S436.621.21 

关键词 BP neural network  QPSO algorithm  Myzus persicae  Occurrence quantity  Prediction model 

文摘内容 In order to effectively predict occurrence quantity of Myzus persicae,BP neural network theory and method was used to establish prediction model for occurrence quantity of M. persicae. Meanwhile,QPSO algorithm was used to optimize connection weight and threshold value of BP neural network,so as to determine the optimal connection weight and threshold value. The historical data of M. persica quantity in Hongta County,Yuxi City of Yunnan Province from 2003 to 2006 was adopted as training samples,and the occurrence quantities of M. persicae from 2007 to 2009 were predicted. The prediction accuracy was 99. 35%,the minimum completion time was 30 s,the average completion time was 34. 5 s,and the running times were 19. The prediction effect of the model was obviously superior to other prediction models. The experiment showed that this model was more effective and feasible,with faster convergence rate and stronger stability,and could solve the similar problems in prediction and clustering. The study provides a theoretical basis for comprehensive prevention and control against M. persicae.