Akademik Veri Yönetim Sistemi
30th IEEE International Conference on Signal Processing and Communications Applications (Sinyal İşleme ve İletişim Uygulamaları Kurultayı) 2022, Karabük, Türkiye, 15 Mayıs - 18 Haziran 2022, cilt.1, ss.1-4
Different methods have been developed to optimize the Adaptive Neural
Fuzzy Inference System, which is used in many fields due to its flexible
structure and trainability. Within the scope of this study, three
different models were produced using two different datasets, using only
the first clustering method, only the second clustering method, and both
the first and second clustering methods. In this study, the Fuzzy
C-Mean Clustering algorithm, which is one of the most efficient methods
used to reduce the number of rules in the rule base of the hybrid
intelligent system is compared with the Highly Connected Subgraphs
algorithm. The models were compared over the square root of the mean
square error, the number of nodes, the number of fuzzy rules, and the
mean training time. As a result of the study, the second clustering
method formed the most efficient result in terms of error rate with
0.084 and 0,008. It has been observed that the average training time of
this method is approximately 31 times longer than the first clustering
method mentioned above, and approximately 52 times longer than the model
in which the first and second clustering methods are used together. In
this study, it has been seen that the first clustering method is more
successful in reducing the rule base by optimizing the second method by
determining more suitable cluster centers. Based on the experimental
results obtained in our study, these two different clustering methods
were compared over three different models. Discussion and scientific
results are included in our study.