Elman动态递归神经网络在树木生长预测中的应用

该文充分考虑树木生长所特有的动态性、随机性和非线性，以及Elman动态递归模型的结构特点，获取北京山区油松解析木生长数据，分别建立了Elman型树木胸径生长和树高生长的神经网络动态模型.研究表明，Elman动态递归模型对非线性问题建模具有很好的拟和性和仿真性，其中，用于胸径生长建模时，其拟和精度达到99.45%，仿真精度达到99.42%；用于树高生长建模时，拟和精度达到 97.30%，仿真精度达到97.29%，而且其拟和和仿真曲线均为“S”形，符合树木生长规律.进一步对Elman动态模型和常规BP静态模型比较发现，Elman模型具有更好的拟和性、预测性和稳定性. 

Application of Elman dynamic recurrent neural network to forecast tree growth.

Fully considering the specific dynamics,randomicity and nonlinear of forest growth,and the structural characteristics of dynamically recursive Elman model,the authors set up Elman type breast-height diameter and tree-height growth of dynamic neural network models with growth data of analytical Pinus tabulaeformis in Beijing Mountainous Area of China.Research showed that it reached very good fitting and simulation in the nonlinear modeling,which was 99.45% of fitting accuracy,99.42% of simulation accuracy used in breast-height diameter growth model;97.30% of fitting accuracy,97.29% of simulation accuracy in the tree-height growth model.Moreover,the fitting and simulation S-shape curve conformed to the rule of tree growth.Further compared the Elman dynamic model with the conventional BP static model,the authors discovered that the Elman model has better fitting,forecasting and stability,which must have the good application prospect in forestry in each kind of dynamic process models.