Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Fuzzy logic-predicate network. / Kosovskaia, Tatiana.
Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. ред. / Vilem Novak; Vladimir Marik; Martin Stepnicka; Mirko Navara; Petr Hurtik. Atlantis Press, 2020. стр. 9-13 (Atlanties Studies in Uncertainty Modeling; Том 1).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Fuzzy logic-predicate network
AU - Kosovskaia, Tatiana
N1 - Publisher Copyright: Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - In many Artrificial Intelligence problems an investigated object is considered as a set of its elements {ω1, . . ., ωt} and is characterized by properties of these elements and relations between them. These properties and relations may be set by predicates p1, . . ., pn. The problems appeared with such an approach become NP-complete or NP-hard ones. To decrease the computational complexity of these problems a hierarchical multi-level description of classes was suggested. A logic-predicate recognition network may be constructed according to such a multi-level description. Such a network recognizes only objects which have been presented in the training set, but it may be easily retrained by a new object. After retraining it may change its configuration i.e., the number of levels and the number of nodes in every level. A modification of such a network is offered in this paper. This modification allows to do a fuzzy recognition of a new object and to calculate the degree of certainty that this object or its part belongs to some class of objects.
AB - In many Artrificial Intelligence problems an investigated object is considered as a set of its elements {ω1, . . ., ωt} and is characterized by properties of these elements and relations between them. These properties and relations may be set by predicates p1, . . ., pn. The problems appeared with such an approach become NP-complete or NP-hard ones. To decrease the computational complexity of these problems a hierarchical multi-level description of classes was suggested. A logic-predicate recognition network may be constructed according to such a multi-level description. Such a network recognizes only objects which have been presented in the training set, but it may be easily retrained by a new object. After retraining it may change its configuration i.e., the number of levels and the number of nodes in every level. A modification of such a network is offered in this paper. This modification allows to do a fuzzy recognition of a new object and to calculate the degree of certainty that this object or its part belongs to some class of objects.
KW - Hierarchical description, logic-predicate recognition network, fuzzy recognition.
UR - http://www.scopus.com/inward/record.url?scp=85090879793&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85090879793
SN - 9789462527706
T3 - Atlanties Studies in Uncertainty Modeling
SP - 9
EP - 13
BT - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
A2 - Novak, Vilem
A2 - Marik, Vladimir
A2 - Stepnicka, Martin
A2 - Navara, Mirko
A2 - Hurtik, Petr
PB - Atlantis Press
T2 - 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
Y2 - 9 September 2019 through 13 September 2019
ER -
ID: 46035193