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- W2378916957 abstract "By diameter of individual tree, age of stand, site index and individual tree’s area as input variables, and by diameter increase of individual tree as output variable, a BP neural network model was created, in which the model structure is 4∶S∶1. The neural network model was trained by growth data of individual trees in thinning stands so that the best network structure is 2∶3∶1 that was named as DMnet. The diameter increase through the model was compared with actual diameter increase. The results show that the average training precision is 90.10% to 97.73% that is higher than usual technology’s 83% to 93%. In the stand management, the model can be used for analysis, computing, simulation, prediction and so on." @default.
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- W2378916957 date "2004-01-01" @default.
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- W2378916957 title "The study on neural network models of individual tree growth in Pinus massoniana plantation" @default.
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