Development of the neural network for the taxation indices

Abstract

The experience of using an artificial neural network for approximating the average height and average diameter of 187 pine stand of various ages (from 7 to 120 years) and density (from 0.4 to 10.7 thousand pieces / ha) is described in the article. As an object of research, there are pure pine stands growing in the ribbon burs of the Altai Krai territory and the Republic of Kazakhstan. All considered stands grow in dry forest growing conditions and have a different origin. Approximation of the data was carried out using the Neural Network Toolbox, which is part of the MATLAB software package. A two-layer neural network with a direct connection, a hidden layer of sigmoid-type neurons and linear output neurons was used in the course of the work. The number of neurons in the hidden layer of the network was chosen experimentally and was chosen equal to five. The aim of the work was to create a mathematical model that allows to determine the average height and average diameter of pine stand of a certain age and density. The article provides a table of the approximated values of the above taxation indices. A comparison of the approximating ability of an artificial neural network and the Mitcherlich function is made, based on the data of absolute and average approximation errors. The conclusion is drawn that the artificial neural network coped with the approximation of the taxation indices better than it was possible to do with the help of the Mitcherlich function. However, the model obtained does not describe the initial data, since the allowable limit of the mean error of approximation was exceeded. © 2018 CEUR-WS. All rights reserved.