QPSO-optimized BP Neural Network to Predict Oc- currence Quantity of Myzus persicae

• 作者: Chen Keping Li Kunlin Qin Xiyun Qiu Jing Yang Yi
• 来源期刊: Plant Diseases and Pests(植物病虫害研究：英文版)
• 学科: 工学
• 关键词: 神经网络预测 BP神经网络 发生量 粒子群算法 桃蚜 优化 预测模型 量子
• 年，卷（期）: 2015,（1）
• 页码范围: 1-3
• 页数: 3页
• 分类号: TP183

摘要

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.