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- W2379782227 abstract "【Objective】The paper developed a growth model of mean diameter(DBH,1.3 m above ground) for Chinese Fir plantation with Back-Propagation network(BP).【Method】Data from even-aged Chinese Fir(Cunninghamia lanceolata(Lamb.) Hook) plantation including conventional measurements of 15 permanent sample plots located at Dagangshan Mountain in Jiangxi province from 1981 to 2008 were collected.BP models employed were one hidden layer with tan-sigmoid function and linearity function as transfer function of hidden and output layer respectively.According to the theory of stand growth,three factors,age(A),site index(SI) and initial planting density(N),were used as predictors for DBH of Chinese Fir.Then BP models were trained by 14 algorithms combining with different structures.Subsequently,the best was selected by comparison with performances of qualitative and quantitative index.On the other hand,generalized Richards's model was introduced as a reference model.Ultimately,the optimal model was examined by independent data from Fujian province.【Result】The optimal BP with one hidden layer containing 3 hidden units was trained by Levenberg-Marquardt algorithm(R2=0.984,MSE=0.196),which was better than Richards model(R2=0.964,MSE=0.433).Practical Result demonstrated calibrated model is also reasonable for Chinese fir plantation in different sites.【Conclusion】BP model is suitable for simulation and prognoses of planted forests of Chinese Fir in which stand variables are within A=6-28 years,SI=12-17 m,N=1 667-9 967 stems/hm2,and ANN generalize well to new data." @default.
- W2379782227 created "2016-06-24" @default.
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- W2379782227 date "2012-01-01" @default.
- W2379782227 modified "2023-09-24" @default.
- W2379782227 title "Modelling tree diameter growth for Chinese Fir plantations with Neural Networks" @default.
- W2379782227 hasPublicationYear "2012" @default.
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