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dc.contributor.authorChrysostomou, Chrysostomosen
dc.contributor.authorPitsillides, Andreasen
dc.contributor.editorHassanien A.-E.en
dc.contributor.editorAbraham A.en
dc.contributor.editorHerrera F.en
dc.creatorChrysostomou, Chrysostomosen
dc.creatorPitsillides, Andreasen
dc.date.accessioned2019-11-13T10:39:23Z
dc.date.available2019-11-13T10:39:23Z
dc.date.issued2009
dc.identifier.issn1860-949X
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/53766
dc.description.abstractThe problem of network congestion control remains a critical issue and a high priority, especially given the increased demand to use the Internet for time/delay-sensitive applications with differing Quality of Service (QoS) requirements (e.g. Voice over IP, video streaming, Peer-to-Peer, interactive games). Despite the many years of research efforts and the large number of different control schemes proposed, there are still no universally acceptable congestion control solutions. Thus, even with the classical control system techniques used from various researchers, these still do not perform sufficiently to control the dynamics, and the nonlinearities of the TCP/IP networks, and thus meet the diverse needs of today's Internet. Given the need to capture such important attributes of the controlled system, the design of robust, intelligent control methodologies is required. Consequently, a number of researchers are looking at alternative non-analytical control system design and modeling schemes that have the ability to cope with these difficulties in order to devise effective, robust congestion control techniques as an alternative (or supplement) to traditional control approaches. These schemes employ fuzzy logic control (a well-known Computational Intelligence technique). In this chapter, we firstly discuss the difficulty of the congestion control problem and review control approaches currently in use, before we motivate the utility of Computational Intelligence based control. Then, through a number of examples, we illustrate congestion control methods based on fuzzy logic control. Finally, some concluding remarks and suggestions for further work are given. © 2009 Springer-Verlag Berlin Heidelberg.en
dc.sourceStudies in Computational Intelligenceen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-66749164722&doi=10.1007%2f978-3-642-01533-5_8&partnerID=40&md5=9f2c676cc90d17104eda79d821bdf9ce
dc.titleFuzzy logic control in communication networksen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1007/978-3-642-01533-5_8
dc.description.volume202
dc.description.startingpage197
dc.description.endingpage236
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :1</p>en
dc.source.abbreviationStud. Comput. Intell.en
dc.contributor.orcidPitsillides, Andreas [0000-0001-5072-2851]
dc.contributor.orcidChrysostomou, Chrysostomos [0000-0002-9287-990X]
dc.gnosis.orcid0000-0001-5072-2851
dc.gnosis.orcid0000-0002-9287-990X


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