Prediction of Attendance Demand in European Football Games: Comparison of ANFIS, Fuzzy Logic, and ANN

Abstract

An artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS) models, and fuzzy rule-based system (FRBS) models are developed to predict the attendance demand in European football games, in this paper. To determine the most successful method, each of the methods is analyzed under different situations. The Elman backpropagation, feed-forward backpropagation, and cascade-forward backpropagation network types are developed to determine the outperforming ANN model. The backpropagation and hybrid optimization methods are used for training fuzzy inference system (FIS) to determine the outperforming ANFIS model. The fuzzy logic model is developed after experimenting different forms of membership functions. To this end, the data of 236 soccer games are used to train the ANN and ANFIS models, and 2017/2018 season's data of these clubs are used to test all of the models. The results of all models are compared with each other and real past data. To assess the performance of each model, two error measures that are Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) are implemented. These measures reveal that the ANN model that has Elman network type outperforms the other models. Finally, the results emphasize that the proposed ANN model can be effectively used for prediction purposes.

Comparing Soft Computing Techniques for Estimating Demand of Season Ticket Holders

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“…Soft computing techniques are utilized for various purposes in the literature. They are capable of predicting results of games [23], selecting players and talents [24], estimating winner of games [25] and demand forecasting [26]. Thus, it is believed that the soft computing techniques are able to produce effective and competitive forecasting results.…”

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought

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Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

“…The standard form of DENNM based architecture for DC and AC excitations are shown in Figs. 3(a) and 3(b), respectively, while mathematically presented in respective equations (10) and (11). The DENNM are formulated based on single input layer, signal hidden layer with log-sigmoid,…”

“…The universal function approximation strength of artificial neural networks (ANNs) has been utilized immensely by the researchers in diverse domain of engineering and technology [1][2][3][4][5]. For example, estimation of STATCOM voltages and reactive powers [6], optimization of heat conduction model of human head [7], optimization of an irreversible thermal engine [8], estimation of underwater inherent optical characteristics [9], prediction of attendance demand in games [10], nonlinear system based on elliptic partial differential equations [11] and optimization of credit classification analysis problems [12]. Recently, the use of stochastic solvers (SS) for effective solution of nonlinear systems based on differential equation has been reported broadly [13][14][15][16].…”