CAJ | 학술논문

考虑到海防林环境气候、动植物组成的特殊性及林业害虫发生的非线性、复杂性、无规律性，通过重构非线性时间序列的相空间并将输出向量作为广义回归神经网络（GRNN, Generalized Regression Neural Network）的输入向量对海防林害虫的发生进行预测；并使用仙居县1983-2011年的马尾松毛虫（Dendrolimus punctatus）实测数据对该预测方法进行仿真检验：1983-2007年虫害数据用于重构相空间及训练神经网络，而2008-2011年的数据用于虫害发生的预测检验。结果表明，在将仙居县马尾松毛虫的危害程度划分为四个等级的前提下，预测方法预测精度较高，除了对2008年马尾松毛虫危害程度的预测出现一个数量级的偏差外，其余的预测准确率均为100%。
고필도해방림배경기후、동식물조성적특수성급임업해충발생적비선성、복잡성、무규률성，통과중구비선성시간서렬적상공간병장수출향량작위엄의회귀신경망락（GRNN, Generalized Regression Neural Network）적수입향량대해방림해충적발생진행예측；병사용선거현1983-2011년적마미송모충（Dendrolimus punctatus）실측수거대해예측방법진행방진검험：1983-2007년충해수거용우중구상공간급훈련신경망락，이2008-2011년적수거용우충해발생적예측검험。결과표명，재장선거현마미송모충적위해정도화분위사개등급적전제하，예측방법예측정도교고，제료대2008년마미송모충위해정도적예측출현일개수량급적편차외，기여적예측준학솔균위100%。
In this paper, reconstructed the phase space of the nonlinear time series and used the output vector as the input vector of Generalized Regression Neural Network (GRNN) to predict the occurrence of coastal protection forest pests.Thespecific method wasusingwavelet transform, noise reduction processingoftheraw data, then calculate thetime delay,embedding dimension and Lyapunov exponent, reusedcalculateddata reconstruction phase space. Finally,using theGRNNto predict thetest. Simulationtestdata was from theXianjuCounty from1983 to 2011. Specific ideas:pestdata of 1983-2007 forthereconstruction phase spaceandtrain the neural network, data of 2008-2011forpestprediction test. The result showed that theprediction has higherprediction accuracy, 2008(forecastdeviationisonlyan order of magnitude), therestoftheforecastaccuracies are all 100%. In short,thephase space reconstructionandGeneralizedRegression NeuralNetworksare allpopularmethodto researchnonlinear problems, this papercombined them asanew attempttopredicttheoccurrenceofcoastal protection forestpests.