Abstract

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.

中文翻译：

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基于回归树的神经网络模型综合

摘要

考虑了通过实验获得的观察结果进行依赖性建模的问题。目的是开发用于神经网络模型综合的方法，以实现自动化，简化和加速模型构建。开发了神经网络模型综合的数学支持。它包含一组将样本转换为决策树或回归树的方法，在此基础上形成神经网络结构并调整参数。进行了解决实际问题的实验。他们的结果证实了所提方法的有效性。实验结果可以推荐开发的方法来解决基于先例构建神经网络模型的问题。