Knowledge based modelling of complex interconnected systems

Boriana Vatchova; David Sanders; Mo Adda; Alexander Gegov

doi:10.1109/BdKCSE48644.2019.9010612

Knowledge based modelling of complex interconnected systems

Boriana Vatchova, David Sanders, Mo Adda, Alexander Gegov

School of Computing

Bulgarian Academy of Sciences

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

156 Downloads (Pure)

Abstract

The present paper discusses the use of intelligent control for modelling complex interconnected processes. The latter usually have many inputs and outputs and can be found in various areas of application. While part of the inputs are measurable, others are not due to the presence of stochastic environmental factors. For this reason such kind of processes operate under uncertainty. The latter is addressed in this paper by intelligent systems that use probabilistic and fuzzy network structures.

Original language	English
Title of host publication	Proceedings of the 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	4
ISBN (Electronic)	978-1-7281-6481-6
ISBN (Print)	978-1-7281-6482-3
DOIs	https://doi.org/10.1109/BdKCSE48644.2019.9010612
Publication status	Published - 27 Feb 2020
Event	Big Data, Knowledge and Control Systems Engineering 2019 - Sofia, Bulgaria Duration: 21 Nov 2019 → 22 Nov 2019 https://ieeexplore.ieee.org/servlet/opac?punumber=8977239

Conference

Conference	Big Data, Knowledge and Control Systems Engineering 2019
Abbreviated title	BdKCSE
Country/Territory	Bulgaria
City	Sofia
Period	21/11/19 → 22/11/19
Internet address	https://ieeexplore.ieee.org/servlet/opac?punumber=8977239

Keywords

fuzzy networks
logical probabilistic networks
logical probability functions
noissn

Access to Document

10.1109/BdKCSE48644.2019.9010612

IEEE_BigDataConference_updated_11.11.2019
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript (Post-print), 533 KB

Top two per cent of researchers in the world
Sanders, David (Recipient), 2 Nov 0001
Prize: National/international honour

Cite this

@inproceedings{1305f930599c4e01bc6866f3a7bd7837,

title = "Knowledge based modelling of complex interconnected systems",

abstract = "The present paper discusses the use of intelligent control for modelling complex interconnected processes. The latter usually have many inputs and outputs and can be found in various areas of application. While part of the inputs are measurable, others are not due to the presence of stochastic environmental factors. For this reason such kind of processes operate under uncertainty. The latter is addressed in this paper by intelligent systems that use probabilistic and fuzzy network structures.",

keywords = "fuzzy networks, logical probabilistic networks, logical probability functions, noissn",

author = "Boriana Vatchova and David Sanders and Mo Adda and Alexander Gegov",

year = "2020",

month = feb,

day = "27",

doi = "10.1109/BdKCSE48644.2019.9010612",

language = "English",

isbn = "978-1-7281-6482-3",

booktitle = "Proceedings of the 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "Big Data, Knowledge and Control Systems Engineering 2019, BdKCSE ; Conference date: 21-11-2019 Through 22-11-2019",

url = "https://ieeexplore.ieee.org/servlet/opac?punumber=8977239",

}

Vatchova, B, Sanders, D , Adda, M & Gegov, A 2020, Knowledge based modelling of complex interconnected systems. in Proceedings of the 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)., 9010612, Institute of Electrical and Electronics Engineers Inc., Big Data, Knowledge and Control Systems Engineering 2019, Sofia, Bulgaria, 21/11/19. https://doi.org/10.1109/BdKCSE48644.2019.9010612

TY - GEN

T1 - Knowledge based modelling of complex interconnected systems

AU - Vatchova, Boriana

AU - Sanders, David

AU - Adda, Mo

AU - Gegov, Alexander

PY - 2020/2/27

Y1 - 2020/2/27

N2 - The present paper discusses the use of intelligent control for modelling complex interconnected processes. The latter usually have many inputs and outputs and can be found in various areas of application. While part of the inputs are measurable, others are not due to the presence of stochastic environmental factors. For this reason such kind of processes operate under uncertainty. The latter is addressed in this paper by intelligent systems that use probabilistic and fuzzy network structures.

AB - The present paper discusses the use of intelligent control for modelling complex interconnected processes. The latter usually have many inputs and outputs and can be found in various areas of application. While part of the inputs are measurable, others are not due to the presence of stochastic environmental factors. For this reason such kind of processes operate under uncertainty. The latter is addressed in this paper by intelligent systems that use probabilistic and fuzzy network structures.

KW - fuzzy networks

KW - logical probabilistic networks

KW - logical probability functions

KW - noissn

UR - http://www.scopus.com/inward/record.url?scp=85082298510&partnerID=8YFLogxK

U2 - 10.1109/BdKCSE48644.2019.9010612

DO - 10.1109/BdKCSE48644.2019.9010612

M3 - Conference contribution

SN - 978-1-7281-6482-3

BT - Proceedings of the 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Big Data, Knowledge and Control Systems Engineering 2019

Y2 - 21 November 2019 through 22 November 2019

ER -

Knowledge based modelling of complex interconnected systems

Abstract

Conference

Keywords

Access to Document

Fingerprint

Prizes

Top two per cent of researchers in the world

Cite this