Algorithm for Constructing Logical Operations to Identify Patterns in Data

Abstract

Neural networks have proven themselves in solving problems when the input and output data are known, but the cause and effect relationship between them is not obvious. A well-trained neural network will find the right answer to a given request, but will not give any idea about the rules that form this data. The paper proposes an algorithm for constructing logical operations, in terms of multi-valued logic, to identify hidden patterns in poorly formalized areas of knowledge. As the basic elements are considered many functions of the multi-valued logic of generalized addition and multiplication. The combination of these functions makes it possible to detect relationships in the data under study, as well as the ability to correct the results of neural networks. The proposed approach was considered for classification problems, in the case of multidimensional discrete features, where each feature can take k-different values and is equivalent in importance to class identification.