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Optimization of Membership Function of Fuzzy Rules Generated using Subtractive Clustering

Author : Hussein A. Lafta and Zahraa A. Mohammed

Pages : 821-824
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Fuzzy rules regarded as a good way to represent the knowledge in many type of problems. It shorten the facts found in the problem at hand in form of IF…THEN, and the membership function regarded as basic part in the structure of these rules. The work is divided into three parts: first one is dealing with clustering process to extract the centers values; the estimation of the centers from multidimensional data set is done by using subtractive clustering algorithm. These centers are converting to fuzzy rules in the rule base, after applying a Gradient Descent method fortuning the membership function parameters. The tuning and applying Fuzzy Inference System are the second and third stage of this work. The scope of the work is heart disease diagnose.

Keywords: Fuzzy Rules, Subtractive Algorithm, Sugeno Inference System, Gradient Descent Method.

Article published in International Journal of Current Engineering and Technology, Vol.6, No.3 (June-2016)


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