Neural Network Model Synthesis Based on a Regression Tree

The problem of dependency modeling by experimentally obtained observations is considered. The objective is to develop methods for neural network model synthesis allowing to automatize, simplify and speed-up model building. The mathematical support for neural network model synthesis is developed. It contains set of methods that transform the sample into a decision tree or a regression tree, on the basis of which the neural network structure is formed and the parameters are adjusted. The experiments on practical problems solving were carried out. Their results were confirmed the efficiency of the proposed methods. The results of the experiments allow to recommend the developed methods for solving the problems of constructing neural network models on precedents.