The paper describes an approach in which the decision-making process of an artificial neural network is interpreted by a fuzzy logic system. A neural network and a fuzzy system are automatically designed with the use of the self-configuring evolutionary algorithms. Experiments are carried out on classification tasks. As a result, it is shown that the building of a fuzzy system on the inputs and outputs of a neural network allows one to build an interpreted rule base of a smaller size, as if this rule base were built on the data of the original problem. In addition, the accuracy of such a system is comparable to the accuracy of a fuzzy system trained on the original task. As a result, the researcher has a neural network with high accuracy of solving the problem, as well as a fuzzy system explaining the neural network's decision-making process. The article presents some constructed rule bases and neural networks for interpretation of which they were built.

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The article describes the approach of resource redistribution for training artificial neural networks in the process of evolution of the structure of these networks. The results of solving classification problems with and without the proposed approach are presented. It is shown that in a number of tasks, the proposed method allows achieving better classification accuracy relative to the standard approach when using an equal amount of resources.