New Methods of Network Modelling Using Parallel-Hierarchical Networks for Processing Data and Reducing Erroneous Calculation Risk

This paper proposes a new type of parallel-hierarchical network – a machine learning technology based on the completion of G-transformations. The network contains horizontal and vertical branches, which create a hierarchical structure. Each vertical and horizontal branch undergoes Gtransformation, which functions by calculating the differences of its elements at every step, and on selected elements. The selected elements are multiplied by the quantity of received non-zero differences. Elements calculated in this way present input data for further network transformations. When the horizontal and vertical branches are formed, their elements shift in time, which determines the formation of tail and intermediate network elements. The risk of erroneous calculations is reduced in a parallel-hierarchical network because when processing information in the presented network, the sum of the resulting elements, i.e. tail elements, are equal to the sum of the input network elements. This presents the ability to lower the risk of erroneous calculations, which assists in controlling the equality of the sums of the tail elements and the sums of the input elements. The obtained results can be used to solve a wide range of problems in various systems that require complex operations and risk assessment, such as comparison between or partial searches of digital images.