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Fuzzy logic-predicate network. / Kosovskaia, Tatiana.

Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. ed. / Vilem Novak; Vladimir Marik; Martin Stepnicka; Mirko Navara; Petr Hurtik. Atlantis Press, 2020. p. 9-13 (Atlanties Studies in Uncertainty Modeling; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Kosovskaia, T 2020, Fuzzy logic-predicate network. in V Novak, V Marik, M Stepnicka, M Navara & P Hurtik (eds), Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. Atlanties Studies in Uncertainty Modeling, vol. 1, Atlantis Press, pp. 9-13, 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019, Prague, Czech Republic, 9/09/19. <https://www.atlantis-press.com/proceedings/eusflat-19/articles >

APA

Kosovskaia, T. (2020). Fuzzy logic-predicate network. In V. Novak, V. Marik, M. Stepnicka, M. Navara, & P. Hurtik (Eds.), Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 (pp. 9-13). (Atlanties Studies in Uncertainty Modeling; Vol. 1). Atlantis Press. https://www.atlantis-press.com/proceedings/eusflat-19/articles

Vancouver

Kosovskaia T. Fuzzy logic-predicate network. In Novak V, Marik V, Stepnicka M, Navara M, Hurtik P, editors, Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. Atlantis Press. 2020. p. 9-13. (Atlanties Studies in Uncertainty Modeling).

Author

Kosovskaia, Tatiana. / Fuzzy logic-predicate network. Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. editor / Vilem Novak ; Vladimir Marik ; Martin Stepnicka ; Mirko Navara ; Petr Hurtik. Atlantis Press, 2020. pp. 9-13 (Atlanties Studies in Uncertainty Modeling).

BibTeX

@inproceedings{8fc74aceca644a5fa2d39815bb139c6a,
title = "Fuzzy logic-predicate network",
abstract = "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.",
keywords = "Hierarchical description, logic-predicate recognition network, fuzzy recognition.",
author = "Tatiana Kosovskaia",
note = "Publisher Copyright: Copyright {\textcopyright} 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.; 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 ; Conference date: 09-09-2019 Through 13-09-2019",
year = "2020",
language = "English",
isbn = "9789462527706",
series = "Atlanties Studies in Uncertainty Modeling",
publisher = "Atlantis Press",
pages = "9--13",
editor = "Vilem Novak and Vladimir Marik and Martin Stepnicka and Mirko Navara and Petr Hurtik",
booktitle = "Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019",
address = "Netherlands",

}

RIS

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