vertical group methods

It is shown that for the pro­ces­sing of in­tensi­ve da­ta flows in in­dustry (ma­na­ge­ment of techno­lo­gi­cal pro­ces­ses and complex ob­jects), energy (op­ti­mi­za­ti­on of lo­ad in po­wer grids), mi­li­tary af­fa­irs (techni­cal vi­si­on, mo­bi­le ro­bot traf­fic control, cryptog­raphic da­ta pro­tec­ti­on), transport (traf­fic ma­na­ge­ment and en­gi­ne), me­di­ci­ne (di­se­ase di­ag­no­sis) and instru­men­ta­ti­on (pat­tern re­cog­ni­ti­on and control op­ti­mi­za­ti­on) the re­al-ti­me hardwa­re neu­ral net­works with high ef­fi­ci­ency of eq­uipment use sho­uld be appli­ed.