Neural Network Density Effect Model of Cunninghamia lanceolata Plantation

The characteristics of arbitrary nonlinear mapping approximation of the artificial neural network was applied to establish the density effect model of Cunninghamia lanceolata plantation, and the parameters of the artificial neural network were obtained by using immune evolutionary algorithm. The simulation results showed that the prediction errors of the stand volume and mean DBH calculated by the artificial neural network based density effect model of C. lanceolata plantation were small, indicating that the modeling method was scientific and reasonable. This model was proved to be better than the traditional density effect models to a certain extent, it would be worthy of being extended to the stand density control for C. lanceolata plantations.