Clustering Methods Comparison for Optimization of Adaptive Neural Fuzzy Inference System

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.