Formulizing the Fuzzy Rule for Takagi-Sugeno Model in Network Traffic Control

Mohammad Alhihi1, *, Mohammad Reza Khosravi2
1 Department of Communications and Electronic Engineering, Philadelphia University, Amman, Jordan
2 Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran; Computer Engineering Department, School of Engineering, Persian Gulf University, Bushehr, Iran

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© 2018 Alhihi and Khosravi.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Communications and Electronic Engineering, Philadelphia University, Amman, Jordan; Tel: 0798961365; E-mail:



Nowadays, fuzzy logic theory is a popular approach to control network variables in engineering problems such as computer and communication networking. In this research, we formulize a new fuzzy logic-based rule for an important engineering application, i.e., traffic control of communication networks.


In this regard, we propose a new formulization based on a well-known model of traffic control in the networks entitled Takagi-Sugeno. Towards this modeling, we use a typical Fuzzy Neural Network (FNN) with an optimizer based on Genetic Algorithm (GA).


The simulation results of our new model clearly prove that the proposed model and its formulation are approximately according to a theoretically consumed model for the problem. In details, we suppose two arbitrary examples for the problem which have two different assumed solutions, and then, we try to resolve the problem for both conditions based on the model in which the simulations show relatively similar results for both simulation-based and theoretical results in both examples.

Keywords: Fuzzy logic, Complex systems, Network traffic control, Fuzzy Neural Network (FNN), Genetic Algorithm (GA), Takagi-Sugeno approach.