基于改进Elman神经网络的林隙大小预测模型

Model based on modified Elman neural network for forecasting forest gap size

针对林隙大小的时变性、不确定性,及林隙大小与其影响因素存在复杂的非线性关系,采用改进的Elman神经网络对林隙大小建立动态模型。在分析改进的Elman神经网络结构特点、改进算法及训练过程的基础上,选择庞泉沟自然保护区内华北落叶松林、油松林、云杉林为对象,建立了基于改进的Elman神经网络林隙大小动态预测模型。结果表明:所建模型对林隙大小的拟合仿真具有很高的精度,预测效果比较稳定。最后运用此模型预测了3种林分对应调查林隙被填充者完全取代的年限。

A dynamic model for forecasting forest gap size was established using a modified Elman neural network for overcoming the disadvantage of time-variability,uncertainty,and complex nonlinear relationship with its impact factor of forest gap size.Firstly,the structural features,mathematical model and learning algorithm of the modified Elman neural network were examined,and a dynamic model for forecasting forest gap size was then set up based on the modified Elman neural network by selecting Larix principis-rupprechtii,Spruce,Pinus tabulaeformis forest in Pangquangou Nature Reserve as the test objects.The fitting and simulation results showed that the established prediction model is feasible to forest gap size,and three kinds of forest gaps were lastly investigated using this model