Fuzzy rule-based modeling in queuing systems

Control applications in systems involve the kinds of problems for which fuzzy logic has had the greatest success and acclaim. Hence, design and control of the queuing systems with fuzzy concept will be more realistic and applicable in real lives.In this thesis, a new hard clustering method is proposed to provide objective knowledge for the fuzzy queuing systems. 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. Some applications of the proposed method including calculations of performances and cost analysis are presented and the results are discussed.Keywords: queuing system, fuzzy rule base, clustering, linear controller, performance.