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Bulanık SMRGT yönteminin pratik uygulamaları

Practical Applications of Fuzzy SMRGT Method

Journal Name:

  • Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi

Publication Year:

  • 2017

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In developing any fuzzy model, construction of the membership functions and generating fuzzy rules are very important. There are many relevant algorithms, i. e. Genetic Algorithm, Artificial Neural Network, Kalman Filters, and many other new statistical or graphical approaches in the literature. However, these approaches do not help in determination of both the fuzzy rules and membership functions together. Additionally, since the difficulties in the usage of these methods many researchers hesitate to use them. Therefore, instead of such methods, the “training and error” approaches are still preferred. In the present study, two applications of a new methodology namely SMRGT, which developed for determining both the membership functions and generating fuzzy rules for a fuzzy system having triangular and trapezoidal membership functions with sentroid deffuzzification method is presented. The SMRGT method was first presented by Toprak (2009). The method can be used only with the "center of gravity" refinement method, both in terms of membership functions (triangle / trapezoid) and fuzzy rules. The difference is that the method often does not need trial-and-error processes; Although this process will be extremely short and will require little processing. In this study, what to do when applying the method is stated in order. A sample application has been given to show the place of this sequence in practice. In practice, it is aimed to calculate the flow depending on the cross-sectional flow velocity and the cross-sectional area of the flow. After the membership functions and rules have been defined, the fuzzy system is set up in the program (MATLAB). The resulting application, graphics are obtained. And this graphs show that the SMRGT method is successful. This study shows; by using the method, mathematical functions can be obtained and the appropriate data can be derived from this function. And so it can be reliably used in the determination of membership functions and the assignment of fuzzy rules without the need for other methods.
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Abstract (Original Language): 
Bulanık modellemede, üyelik fonksiyonları ve bulanık kuralların uygun bir şekilde belirlenmesi, denemeyanılma sürecinin kısa tutulabilmesi ve modelin başarısı açısından birinci derecede önemlidir. Gerek üyelik fonksiyonlarının belirlenmesine gerek kuralların atanmasına ilişkin literatürde çeşitli yaklaşımlara rastlamak mümkündür (Genetik Algoritma, Yapay Sinir Ağları, Kalman Filtresi, çeşitli istatistiksel ve grafiksel yaklaşımlar gibi). Fakat bu algoritmalar genellikle, üyelik fonksiyonları ve bulanık kuralların belirlenmesi için ayrı ayrı geliştirilmiştir. Bu yöntemlerin bir kısmı ayrıca paket programları ve geniş zaman ve işlem hacmini gerektirirken bir kısmı ise deneme-yanılma yönteminden tümü ile kurtaracak kadar iyi sonuç verememektedir. Bu çalışmada ise hem üyelik fonksiyonlarının (üçgen/trapez) belirlenmesinde hem de bulanık kuralların atanmasında sentroid durulaştırma yöntemi ile kullanılabilecek SMRGT adında literatürde yeni olan bir yaklaşımın iki pratik uygulaması sunulmaktadır. Uygulama sonucunda, yöntemin başarılı olduğu görülmüştür.
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