关键词: BP神经网络, 修正函数, 小班蓄积预估模型, 模型库, JAVA和R语言编程智能化软件

Abstract: Stand volume is an important indicator to measure stand productivity of subcompartment. Using subcompartment data of Larix principis-rupprechtii Mayr plantation, this paper established two stand volume prediction models based on BP neural network and multiple regression, with stand age, site index and stand density as input variables and stand volume as the output variable. At the same time, the prediction results of the two models were compared. The results showed that: ① the optimal parameters combination of the BP neural network was the three layer network structure included the input layer of three neurons. the hidden layer of ten neurons and one neuron, the output layer of one neuron. The batch gradient descent method with momentum method was used in R language or Levevberg-Marquardt method in MATLAB software; ② in the multivariate regression model, the combination of “Logistic + power function”, V=SI0.977 2N0.510 3 0.500 1/\[1+44.226 1exp(-0.146 6t)\] in the modified function based on the growth theory equation was the best, and the coefficient of model fitting determination was R2=0.721 8; ③ in the prediction accuracy the BP model had optimal performance, followed by the multiple regression model, and finally volume table. Based on the above research, in order to improve the practicability of BP model, through JAVA and R language programming, BP neural network prediction model of subcompartment volume was constructed and stored in the stand volume yield prediction model of knowledge base, which could achieve the development of the classic mathematical model from the form of intelligent software, make the forestry staff use the intelligent system to easily fit and call the better fitting effect of the model to improve the BP model in the actual production of the operability, and provide decision support for forest management operations.

Key words: BP neural network, correction function, subcompartment volume prediction model, model library, Intelligent software of JAVA and R language programming