CAJ | 학술논문

以福建省将乐县国有林场29块杉木人工林实测数据为例，运用BP 神经网络建模技术建立树高预测模型。分别确定输入量和隐层节点数，再经训练和优选，得到的最优模型结构为2∶5∶1，决定系数为0．9023，均方误差为1．7842。结合传统的两个标准树高曲线方程，利用检验数据分别对模型进行验证。结果表明：BP神经网络模型不管是拟合效果还是预测效果都明显优于传统方程，可以作为有效的树高预测技术。
이복건성장악현국유림장29괴삼목인공림실측수거위례，운용BP 신경망락건모기술건립수고예측모형。분별학정수입량화은층절점수，재경훈련화우선，득도적최우모형결구위2∶5∶1，결정계수위0．9023，균방오차위1．7842。결합전통적량개표준수고곡선방정，이용검험수거분별대모형진행험증。결과표명：BP신경망락모형불관시의합효과환시예측효과도명현우우전통방정，가이작위유효적수고예측기술。
We used the data of 29 plots of Chinese fir located in national forest farm of Jiangle in Fujian Province to build height prediction model by BP neural network .First, the input variable and the hidden nodes were determined , then, by training and optimization, an optimum model was developed, with a model structure of 2∶5∶1, a determinate coefficient of 0.902 3 and error of mean square of 1.784 2.And then, it was compared with two traditional generalized height-diameter equations, the validation datasets were used to test the models , respectively .The fitting effect and prediction effect of BP neural network model are better than those of traditional equations , and BP neural network model can be used as effective tree height pre-diction technology .