Abstract: A binary learning system characterized by consisting of an input layer having binary input terminals, a coupling layer, a first binary gate layer with first similar logical elements, a second binary gate layer with second similar logical elements, and an output layer, so as to form a learning network, in that each coupling condition between the adjacent layers limited to one way directing from their inlet side to the outlet side, and each layer has independent routes without mutual coupling conditions, the coupling layer having means for selecting either one of a direct coupling condition and a coupling condition routed through an inverter, relative to routes from the respective signal units in the input layer to the respective signal units in the first binary gate layer, in such manner that the selected coupling condition is adapted to eliminate or decrease the respective errors between original output signals at the output layer and monitor signals in the learning network.

Abstract: This invention provides learning methods in binary systems by modifying the connected states of the circuit among each basic binary gate in binary combined logical and sequential circuits composed with basic binary gates such as AND, OR, NOT, NAND, NOR and EXOR gates. As the pseudo-neuron theory and the pseudo-potential energy theory are skillfully introduced, it is possible to attain specified learning effects during a very short learning period. Further, as implementation of the learning methods into the conventional computer and other digital equipment is simple, it is expected to be used widely in wide application, for example, such as in image processing, voice processing or natural word processing.