【摘要】 采用综合分析法和相关分析法选定每公顷蓄积量、标准木年龄、标准木树高、标准木胸径为输入变量,以标准木的上个生长季的材积连年生长量为输出变量,建立三层结构的油松(Pinus tabulaeformisCarr.)人工林单木材积生长量BP神经网络模型。利用北京山区78块各类型油松人工林样地调查数据对模型进行训练和仿真模拟。通过综合比较得出网络模型结构为4∶11∶1的网络模型具有最好的模拟效果,训练精度可达94.024%。此模型可以准确、简单、快速的对北京山区油松人工林的单木材积生长量进行有关的分析、计算、模拟和预测。更多还原

【Abstract】 Three-layer BP neural network models for volume growth of single trees in Pinus tabulaeformis plantations in Beijing mountain area were established using current annual increment of standard tree in the previous growing season as the output variable,and volume per hectare,the age,height and diameter at breast height of the standard tree as the input variables which were chosen by the methods of comprehensive analysis and correlation analysis.The neural network models were trained and simulated by the data of 78 sample plots in different types of P.tabulaeformis plantations in Beijing mountain area.Comprehensive comparison of the networks shows that the network model with the structure of 4∶11∶1 has the best simulation results,with a training accuracy of 94.024%.This model can be applied to the analysis,calculation,simulation and prediction of volume growth of single trees in P.tabulaeformis plantations in Beijing mountain area,and it is proved to be an accurate,simple,and rapid model.更多还原