A New Clustering Method with Fuzzy Approach Based on Takagi-Sugeno Model in Queuing Systems
FG Zanjanbar, İ Şentarlı - Computational Linguistics: Concepts …, 2014 - igi-global.com
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 …
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 …
[PDF][PDF] A New Clustering Method with Fuzzy Approach Based on Takagi-Sugeno Model in Queuing Systems
İ Şentarlı - 2013 - scholar.archive.org
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 …
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 …
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