SUMMARY
The article presents the structural-parametric synthesis of an ensemble of neural networks of various architectures based on their individual contribution. Topologies and learning algorithms for each classifier are considered. It is described the algorithm for calculating the individual contribution of each network and the algorithm for selecting networks in the ensemble according to the criteria of accuracy and diversity. In order to simplify the structure of the ensemble, the Complementary Measure method was used. The results of learning of classifiers on training bootstrap samples are presented. The obtained results of the ensemble are compared with the corresponding results of each neural network included in the ensemble separately.