Adaptive Methods for the Structural Optimization of Neural Networks and Their Ensemble for Data Analysis

The article is devoted to studying the effectiveness of ensemble methods of neural network solvers for solving a regression problem. The problem of ensuring the efficiency of using a prediction model based on a set of artificial neural networks, which should provide increased efficiency compared to a single regressor, is considered. One of the requirements to ensure an increase in efficiency is that the solvers in the ensemble must be sufficiently different to ensure that the ensemble model exits the region of the local minimum error of a single solver. For the considered case of constructing neural network ensembles, it is proposed to provide distinction based on the generation of different structures of neural network regressors. To do this, a modification is introduced into the structure of the previously developed probabilistic method for designing neural networks. This modification is based on the use of special coefficients that determine the adaptation of the probability of using various activation functions depending on the presence of those in already formed neural networks in the ensemble. The proposed approach was implemented in a software system and tested using generated datasets and real industrial data sets, described in the article. The results obtained indicate the relatively high efficiency of constructing collective regressors when forming neural networks using the proposed approach while maintaining diversity in conditions of noisy samples.