基于因子分析与神经网络组合的害虫测报研究  

Pest Prediction Research Based on Combining Factor Analysis and Neural Network

作　　者：贾伟宽[1] 王慧[1] 丁世飞[2] 苏春阳[2] 

机构地区：[1]山东农业大学植物保护学院,山东泰安271018 [2]中国矿业大学计算机科学与技术学院,江苏徐州221008

出　　处：《长江大学学报（自科版）（中旬）》2008年第3期5-8,共4页Journal of Yangtze University(Nature Science Edition)

基　　金：国家自然科学基金项目(40574001);中国科学院智能信息处理重点实验室开放基金项目(IIP2006-2)

摘　　要：害虫的发生是非线性动态系统,影响害虫发生的预测因子众多,且存在一定相关性,用神经网络进行预测时,不利于设计与计算。结合因子分析与神经网络的原理,建立基于因子分析与神经网络组合的害虫预测模型,通过因子分析对预测因子进行降维处理,然后将降维后的数据作为网络的输入,经训练后仿真输出预测结果。通过对山东郓城县二代棉铃虫预测的实例分析,证明新模型的预测精度没有降低,网络的收敛速度加快,预测值的误差减小。说明这一模型在农作物的病虫害预测方面有着广阔的应用前景。The appearance of pest is a nonlinear system.There are many predicting factors that influence the appearance of pest.They are interrelated to some extent.When using neural network to predict,it is not propitious for the design and computation.combined the principle of factor analysis and neural network and builds the model basing on the combination of factor analysis and neural network were built.By factor analysis the dimensionality of predicting factors was reduced,and the data after dimension reduction was regarded as the input of the network,then the predicting results after training.By analyzing the second period prediction of Helicoverpa armigera(Hübner) of Yuncheng,Shandong,it is proved that the prediction inaccuracy of the new model is not reduced,the convergence velocity speed up,and the error of prediction value is reduced.It shows that this model has wide application prospects in the aspects of the prediction of plant diseases and insect pests.

关 键 词：害虫 预测预报 组合预测模型 

分 类 号：S431.9[农业科学—农业昆虫与害虫防治]