Comparison of the Effects of Different Dimensional Reduction Algorithms on the Training Performance of Anfis (Adaptive Neuro-Fuzzy Inference System) Model
Journal Title: Cumhuriyet Science journal - Year 2017, Vol 38, Issue 4
Abstract
Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is a hybrid artificial neural network (intelligence) approach that utilizes the ability of artificial neural networks to learn, generalize, paralyze and to derive fuzzy logic. The development of models with large numbers of input variables with ANFIS is not very convenient for applications. Dimension reduction methods are proposed as a solution to this problem. Dimensional Reduction is the method used to represent the data in a lower dimensional space. The reduction of the numbers of the input variables using different size reduction methods and the creation of the optimal solution of the probing with the ANFIS model constitute the framework of this work. In this study, we compared the results produced by different dimension reduction methods and investigated which method is more acceptable for ANFIS training.
Authors and Affiliations
Ahmet Gürkan YÜKSEK, Halil ARSLAN, Oğuz KAYNAR, Emre DELİBAŞ, Abdulkadir ŞEKER
Keywords
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- EP ID EP488466
- DOI 10.17776/csj.347653
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