An artificial neural network (ann), adaptive neurofuzzy inference system (anfis) models, and fuzzy rule-based system (frbs)\nmodels are developed to predict the attendance demand in European football games, in this paper. To determine the most\nsuccessful method, each of the methods is analyzed under different situations. The Elman backpropagation, feed-forward\nbackpropagation, and cascade-forward backpropagation network types are developed to determine the outperforming ann\nmodel. The backpropagation and hybrid optimization methods are used for training fuzzy inference system (fis) to determine the\noutperforming anfis model. The fuzzy logic model is developed after experimenting different forms of membership functions.\nTo this end, the data of 236 soccer games are used to train the ann and anfis models, and 2017/2018 seasonâ??s data of these clubs\nare used to test all of the models. The results of all models are compared with each other and real past data. To assess the\nperformance of each model, two error measures that are Mean Absolute Percent Error (mape) and Mean Absolute Deviation\n(mad) are implemented. These measures reveal that the ANN model that has Elman network type outperforms the other models.\nFinally, the results emphasize that the proposed ANN model can be effectively used for prediction purposes.
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