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Şentarlı, İnci

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https://hdl.handle.net/20.500.12416/9039
Name Variants
Şentarli, I.
Job Title
Yrd. Doç. Dr.
Email Address
senta@cankaya.edu.tr
Main Affiliation
İşletme
Status
Former Staff
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Scholarly Output

3

Articles

1

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191/9

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0

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0.00

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1

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Computational Linguistics: Concepts, Methodologies, Tools, and Applications1
International Journal of Fuzzy System Applications1
Quality Cost Modeling Process for Production Systems1
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Now showing 1 - 3 of 3
  • Thumbnail Image
    Book Part
    A New Clustering Method With Fuzzy Approach Based on Takagi-Sugeno Model in Queuing Systems
    (IGI Global, 2014) Zanjanbar, F.G.; Şentarli, I.
    In this paper, the authors propose a new hard clustering method to provide objective knowledge on field of fuzzy queuing system. In this method, locally linear controllers are extracted and translated into the first-order Takagi-Sugeno rule base fuzzy model. In this extraction process, the region of fuzzy subspaces of available inputs corresponding to different implications is used to obtain the clusters of outputs of the queuing system. Then, the multiple regression functions associated with these separate clusters are used to interpret the performance of queuing systems. An application of the method also is presented and the performance of the queuing system is discussed. © 2014 by IGI Global. All rights reserved.
  • Thumbnail Image
    Conference Object
  • Thumbnail Image
    Article
    A New Clustering Method With Fuzzy Approach Based on Takagi-Sugeno Model in Queuing Systems
    (IGI Global, 2013) Zanjanbar, F.G.; Şentarli, I.
    In this paper, the authors propose a new hard clustering method to provide objective knowledge on field of fuzzy queuing system. In this method, locally linear controllers are extracted and translated into the first-order Takagi-Sugeno rule base fuzzy model. In this extraction process, the region of fuzzy subspaces of available inputs corresponding to different implications is used to obtain the clusters of outputs of the queuing system. Then, the multiple regression functions associated with these separate clusters are used to interpret the performance of queuing systems. An application of the method also is presented and the performance of the queuing system is discussed. Copyright © 2013, IGI Global.