摘要
为了提高害虫发生量预测的精度,提出一种基于混沌理论的害虫发生量非线性预测模型(PSR–LSSVM)。通过相空间重构对害虫发生量时间序列进行重构,将重构后的害虫发生量序列输入到最小二乘支持向量机进行学习,建立害虫发生量预测模型,采用云南省普洱市思茅区和浙江省仙居县的松毛虫发生面积数据对模型性能进行检验。结果表明,松毛虫发生面积预测值与实际发生值十分接近,2个地区松毛虫发生面积预测结果的平均绝对百分误差分别为0.90%和2.44%,预测结果要优于BP神经网络、线性预测模型。
In order to improve the prediction accuracy of pest＇s occurrence quantity, this paper proposed a nonlinear prediction model for pest＇s occurrence quantity based on chaotic theory. Time series for pest＇s occurrence quantity were reconstructed by phase space reconstruction, and then input into the least squares support vector machine to learn and establish prediction model for the pest＇s occurrence quantity, the test experiment is carried out using Dendrolimus punctatus occurrence area data in Simao, Puer and Xianju county, Zhejiang. The results show that the prediction values of Dendrolimus punctatus occurrence area were very close to the actual production, the mean absolute percent error of predicted results for Dendrolimus punctatus occurrence area in in Simao, Puer and Xianju county, Zhejiang were 0.90% and 2.44% respectively, the prediction results were better than that of BP neural network and linear prediction model.
出处
《湖南农业大学学报（自然科学版）》
CAS
CSCD
北大核心
2015年第2期172-176,共5页
Journal of Hunan Agricultural University(Natural Sciences)
基金
国家"973"计划项目(2009CB119200)
公益性行业(农业)科研专项(201303024–04
201303019–02)
湖南省自然科学基金项目(2015jj2041)
关键词
害虫发生量
最小二乘支持向量机
预测模型
相空间重构
pest＇s occurrence quantity
least squares support vector machine
prediction model
phase space reconstruction
