Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Intuitionistic Fuzziness and Parallelism: Theory and Applications
share announcement

Intuitionistic Fuzziness and Parallelism: Theory and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3535

Special Issue Editor

Dr. Velin Andonov

E-Mail Website
Guest Editor
Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str., Block 8, 1113 Sofia, Bulgaria
Interests: mathematical modeling; intuitionistic fuzzy sets; generalized nets; service systems; quality of service

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to disseminate the recent advances in the areas of intuitionistic fuzzy sets (IFSs) and generalized nets (GNs) and their applications in the modelling of parallel processes under uncertainty. The IFS, an extension of the concept of the fuzzy set, is a popular and widely accepted tool for the modelling of uncertainty. It has many applications, some of which are in the areas of decision making under uncertainty, InterCriteria analysis, the modelling of parallel processes through intuitionistic fuzzy GNs, and most recently, in the quality of service (QoS) estimation of service systems. GNs are extensions of Petri nets, which have been proven to be a suitable tool for the modelling of parallel processes with applications in a wide range of areas, such as medicine, artificial intelligence, telecommunications, etc. Despite the extensive work in these fields throughout the past four decades, new theoretical results continue to be published and many new applications are being found.

Some of the subject areas within the scope of this Special Issue are:

  • Extensions of the ordinary GNs.
  • Intuitionistic fuzzy GNs.
  • Intuitionistic fuzzy sets.
  • Intuitionistic fuzzy logic.
  • Intuitionistic fuzzy pairs.
  • Modelling of parallel processes under intuitionistic fuzzy uncertainty.

Authors are welcome to submit original and significant contributions on the above or other closely related subjects.

Dr. Velin Andonov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intuitionistic fuzzy sets
  • generalized nets
  • parallel processes
  • intuitionistic fuzzy logic
  • uncertainty

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 364 KiB  
Article
Generalized Net Model of Heavy Oil Products’ Manufacturing in Petroleum Refinery
by Danail Stratiev, Angel Dimitriev, Dicho Stratiev and Krassimir Atanassov
Mathematics 2023, 11(23), 4753; https://0-doi-org.brum.beds.ac.uk/10.3390/math11234753 - 24 Nov 2023
Viewed by 567
Abstract
Generalized nets (GNs) are a suitable tool for the modeling of parallel processes. Through them, it is possible to describe the functioning and results of the performance of complex real processes running in time. In a series of articles, we consistently describe the [...] Read more.
Generalized nets (GNs) are a suitable tool for the modeling of parallel processes. Through them, it is possible to describe the functioning and results of the performance of complex real processes running in time. In a series of articles, we consistently describe the main processes involved in the production of petroleum products taking place in an oil refinery. The GN models can be used to track the actual processes in the oil refinery in order to monitor them, make decisions in case of changes in the environment, optimize some of the process components, and plan future actions. This study models the heavy oil production process in a refinery using the toolkit of GNs. Five processing units producing ten heavy-oil-refined products in an amount of 106.5 t/h from 443 t/h atmospheric residue feed, their blending, pipelines, and a tank farm devoted to storage of finished products consisting of three grades of fuel oil (very low sulfur fuel oil (0.5%S) —3.4 t/h; low sulfur fuel oil (1.0%S) —4.2 t/h; and high sulfur fuel oil (2.5%S) —66.9 t/h), and two grades of road pavement bitumen (bitumen 50/70 —30 t/h and bitumen 70/100 —2 t/h) are modeled in a GN medium. This study completes the process of modeling petroleum product production in an oil refinery using GNs. In this way, it becomes possible to construct a highly hierarchical model that incorporates the models already created for the production of individual petroleum products into a single entity, which allows for a comprehensive analysis of the refinery’s operations and decision making concerning the influence of various factors such as disruptions in the feedstock supply, the occurrence of unplanned shutdowns, optimization of the production process, etc. Full article
(This article belongs to the Special Issue Intuitionistic Fuzziness and Parallelism: Theory and Applications)
►▼ Show Figures

Figure 1

17 pages, 412 KiB  
Article
Modeling the Production Process of Fuel Gas, LPG, Propylene, and Polypropylene in a Petroleum Refinery Using Generalized Nets
by Danail D. Stratiev, Angel Dimitriev, Dicho Stratiev and Krassimir Atanassov
Mathematics 2023, 11(17), 3800; https://0-doi-org.brum.beds.ac.uk/10.3390/math11173800 - 4 Sep 2023
Cited by 3 | Viewed by 1122
Abstract
The parallel processes involved in the production of refinery fuel gas, liquid petroleum gas (LPG), propylene, and polypropylene, occurring in thirteen refinery units, are modeled by the use of a Generalized Net (GN) apparatus. The modeling of the production of these products is [...] Read more.
The parallel processes involved in the production of refinery fuel gas, liquid petroleum gas (LPG), propylene, and polypropylene, occurring in thirteen refinery units, are modeled by the use of a Generalized Net (GN) apparatus. The modeling of the production of these products is important because they affect the energy balance of petroleum refinery and the associated emissions of greenhouse gases. For the first time, such a model is proposed and it is a continuation of the investigations of refinery process modelling by GNs. The model contains 17 transitions, 55 places, and 47 types of tokens, and considers the orders of fuel gas for the refinery power station, refinery process furnaces, LPG, liquid propylene, and 6 grades of polypropylene. This model is intended to be used as a more detailed lower-level GN model in a higher-level GN model that facilitates and optimizes the process of decision making in the petroleum refining industry. Full article
(This article belongs to the Special Issue Intuitionistic Fuzziness and Parallelism: Theory and Applications)
►▼ Show Figures

Figure 1

14 pages, 477 KiB  
Article
Intuitionistic Fuzzy Deep Neural Network
by Krassimir Atanassov, Sotir Sotirov and Tania Pencheva
Mathematics 2023, 11(3), 716; https://0-doi-org.brum.beds.ac.uk/10.3390/math11030716 - 31 Jan 2023
Cited by 1 | Viewed by 1465
Abstract
The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigation presents in a methodological [...] Read more.
The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigation presents in a methodological way the whole process of IFDNN development, starting with the simplest form—an intuitionistic fuzzy neural network (IFNN) with one layer with single-input neuron, passing through IFNN with one layer with one multi-input neuron, further subsequent complication—an IFNN with one layer with many multi-input neurons, and finally—the true IFDNN with many layers with many multi-input neurons. The formulas for strongly optimistic, optimistic, average, pessimistic and strongly pessimistic formulas for NN parameters estimation, represented in the form of intuitionistic fuzzy pairs, are given here for the first time for each one of the presented IFNNs. To demonstrate its workability, an example of an IFDNN application to biomedical data is here presented. Full article
(This article belongs to the Special Issue Intuitionistic Fuzziness and Parallelism: Theory and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop