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林业科学 ›› 2000, Vol. 36 ›› Issue (zk): 148-153.doi: 10.11707/j.1001-7488.2000S123

• 研究简报 • 上一篇    

毛竹林林分平均胸径模拟预测模型的研究

何东进 洪伟 吴承祯   

  1. 福建林学院资源与环境系,南平353001
  • 收稿日期:1998-06-17 修回日期:1900-01-01 出版日期:2001-01-25 发布日期:2001-01-25

A STUDY ON SIMULATING PREDICTIVE MODEL OF MEAN DBH FOR BAMBOO STANDS

He Dongjin,Hong Wei,Wu Chengzhen   

  1. Fujian College of Forestry Nanping 353001
  • Received:1998-06-17 Revised:1900-01-01 Online:2001-01-25 Published:2001-01-25

关键词: 毛竹, 平均胸径, 人工神经网络, 模拟与预测

Abstract:

The number of bamboo stem at different ages and the mean diameter at breast height(DBH)which are the important target in evaluating productivity of bamboo stand were investigated in 50 plots established in Jianou city, Fujian Province in this paper, and the authors selected the method of artificial neural network to biuld the simulative and predictive model of mean DBH for bamboo stands. Artificial neural network is a good method in handling the overall nonlinear mapping problems between input variables and output ones, which has a wide application in many research fields, such as system simulating, automation controlling, paralleled data processing and so on. In this paper, the input variables were the number of different age and the total number of stand, the output variable was mean DBH for bamboo stands, the number of neurons of hide-level(M) was M=2L+1=3 according to the last document (L is the number of factors of input-level), and the network activity function is Sigmiod function as follows:F(x)=1/(1+e-x). Using the built BP network, the samples were trained until Ej(W1lm,W2mn)=Nn=1(Onj-Ynj)2=min, where Onj and Ynj are output values of network and really values of DBH for bamboo stands respectively,N is the number of trained samples, and Ej is sum of square deviation of BP network. If Ej didn't converge, the weights and thresholds of BP network were adjusted as follow: ΔWij(n+1)=βλjXi+αΔWij(n) and Δηj(n+1)=-βλj+αΔηj(n) .. The results showed that the mean simulative accuracy and the mean predictive accuracy of mean D.B.H BP model for bamboo stands were all satisfactory, which were 89.95% and 89.26% respectively. Therefore, it provided a scientific basis for evaluating the productivity and realizing high yield for bamboo stands.

Key words: Phyllostachys pubescens, Mean DBH, Artificial nerual network, Simulation and prediction