Research output: Contribution to journal › Article › peer-review
Fuzzy recognition by logic-predicate network. / Косовская, Татьяна Матвеевна.
In: Advances in Science, Technology and Engineering Systems Journal, Vol. 5, No. 4, 2020, p. 686-699.Research output: Contribution to journal › Article › peer-review
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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 -
ID: 62255976