Various-valued logic functions and neural networks application in decision-making systems

In this paper we propose a method for representing various-valued logic function as a logical neural network. This logical neural network will allow to preserve the totality of cause-and-effect relationships identified using various-valued logic functions within a given specified area. Thus, it becomes possible to transfer a logical algorithm to detect hidden patterns in a given specified area, in case when the values of logical variables are not well-defined but are values obscured between zero and one. These logic operations are implemented by special logic neural cells: conjunctors and disjunctors.