Creating distributed artificial neural networks based on orthogonal transformations

The article addresses the issue of separating input information of artificial neural networks into modules using orthogonal transformations. This separation enables modular organization of neural networks with layer separation, facilitating the use of the proposed approach for distributed computing. Such an approach is required for organizing the operation of neural networks in fog and edge computing environments, as well as for high-performance computing across multiple low-performance computational nodes. The possibility of cross-layer separation of artificial neural networks using orthogonal transformations is theoretically substantiated, and practical examples of such an approach are provided. A comparison of the characteristics of modular neural networks using various types of orthogonal transformations, including the Haar wavelet transform, is conducted.