TY - JOUR

T1 - Fuzzy recognition by logic-predicate network

AU - Косовская, Татьяна Матвеевна

N1 - Funding Information: The author is grateful to Vakhtang Kvaratskhelia from Muskhelishvili Institute of Computational Mathematics of the Georgian Technical University, whose question at the conference in 2017 allowed this paper to be born. The author thanks the anonymous referees for their useful suggestions. Publisher Copyright: © 2020 ASTES Publishers. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - The paper presents a description and justification of the correctness of fuzzy recognition by a logic-predicate network. Such a network is designed to recognize complex structured objects that can be described by predicate formulas. The NP-hardness of such an object recognition requires to separate the learning process, leaving it exponentially hard, and the recognition process itself. The learning process consists in extraction of groups of features (properties of elements of an object and the relations between these elements) that are common for objects of the same class. The main result of a paper is a reconstruction of a logic-predicate recognition cell. Such a reconstruction allows to recognize objects with descriptions not isomorphic to that from a training set and to calculate a degree of coincidence between the recognized object features and the features inherent to objects from the extracted group.

AB - The paper presents a description and justification of the correctness of fuzzy recognition by a logic-predicate network. Such a network is designed to recognize complex structured objects that can be described by predicate formulas. The NP-hardness of such an object recognition requires to separate the learning process, leaving it exponentially hard, and the recognition process itself. The learning process consists in extraction of groups of features (properties of elements of an object and the relations between these elements) that are common for objects of the same class. The main result of a paper is a reconstruction of a logic-predicate recognition cell. Such a reconstruction allows to recognize objects with descriptions not isomorphic to that from a training set and to calculate a degree of coincidence between the recognized object features and the features inherent to objects from the extracted group.

KW - Fuzzy predicate network

KW - Logic-predicate approach to AI

KW - Predicate calculus

KW - Predicate network

UR - http://www.scopus.com/inward/record.url?scp=85092898142&partnerID=8YFLogxK

U2 - 10.25046/aj050482

DO - 10.25046/aj050482

M3 - Article

AN - SCOPUS:85092898142

VL - 5

SP - 686

EP - 699

JO - Advances in Science, Technology and Engineering Systems

JF - Advances in Science, Technology and Engineering Systems

SN - 2415-6698

IS - 4

ER -