基于径向基小波网络的二代棉铃虫卵峰日预测模型

Forecasting model for the oviposition peak day in the second generation of Helicoverpa armigera(Lepidoptera:Noctuidae) based on radial basis wavelet network

为了提高害虫预报的准确率,将径向基小波网络首次引入农作物害虫预测预报领域,改进了径向基小波网络的学习算法,使之适合于害虫预测的应用:利用径向基小波函数族时、频两域支撑完全或部分覆盖被分析数据序列时、频两域支撑的原理来确定小波函数族尺度参数和平移参数取值;根据中心向量之间的欧式距离大小来初步筛选隐含层神经元。在实例分析中,本文利用1966-1995年山东省惠民县棉铃虫Helicoverpa armigera的监测数据建立了基于径向基小波网络的2代棉铃虫卵量峰值日期预测模型,利用1996-2000年的监测数据对模型进行了检验。检验结果表明:在5年的预测数据中,4年的预测数据偏差在3d以内,另外1年的预测数据偏差4d,预测效果令人满意。本文为害虫预测预报研究提供了一种可行的新方法。

To improve the accuracy of crop pest forecasting,this paper introduced and applied radial basis wavelet network into the area of crop pest forecasting for the first time. The author modified the learning algorithms of radial basis wavelet network for application in pest forecasting. The scale and translation parameters were determined by the theory that time-frequency support of analyzed data sequence is covered with time-frequency support of radial basis wavelet functions. Based on the Euclidean distance between central vectors,the hidden-layer neurons are selected preliminarily. At case study,the investigation data of Helicoverpa armigera in Huimin,Shandong between 1966 and 1995 were used to establish the forecasting model of oviposition peak day in the second generation of H. armigera based on radial basis wavelet network,while the investigation data between 1996 and 2000 were used to test the model. The test results showed that the forecasting deviation of four years was less than three days and the forecasting deviation of one year was four days. The forecasting results proved satisfactory. This paper developed a new studying method for crop pest forecasting.