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

Qiu Jing; Yang Yi; Qin Xiyun; Li Kunlin; Chen Keping
February 2015
Plant Diseases & Pests;Feb2015, Vol. 6 Issue 1, p1
Academic Journal
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.


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