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 E_j(W¹_lm,W²_mn)=Nn=1(O_nj-Y_nj)²=min, where O_nj and Y_nj are output values of network and really values of DBH for bamboo stands respectively,N is the number of trained samples, and E_j is sum of square deviation of BP network. If E_j didn't converge, the weights and thresholds of BP network were adjusted as follow: ΔW_ij(n+1)=βλ_jX_i+αΔW_ij(n) and Δη_j(n+1)=-βλ_j+αΔη_j(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