[Neural network based on modified simplex method and its application in studying forest self-thinning].
The mechanism of forest self-thinning is generally nonlinear and dynamic, and the artificial neural network has the characteristic of expressing arbitrary nonlinear mapping. In this paper, the feasibility and limitation of artificial neural network used to simulating forest self-thinning was expounded, and the principle and algorithms of the neural network model based on modified simplex method (BP-MSM mixed algorithms) for modeling forest self-thinning were described. Its applications in self-thinning of Populus tremula natural forest Cunninghamia lanceolata plantation were illustrated. The results of forest self-thinning examples show the BP-MSM mixed algorithms were satisfactory in simulating forest self-thinning, and its precision was higher, which develops the method and theory of artificial neural network, and enriches the simulating method of forest self-thinning.
Wu
C
C
Department of Resources and Environment, Fujian Forestry College, Nanping 353001. zjwucz@public.npptt.fj.cn
Hong
W
W
D000465:Algorithms; D003198:Computer Simulation; D032462:Cycadopsida / Q000502:physiology*; D016571:Neural Networks, Computer; D031308:Salicaceae / Q000502:physiology*