Department of Business Administration, Adiyaman University, 02040 Adiyaman, Turkey.
Department of Industrial Engineering, Cukurova University, 01330 Adana, Turkey.
Comput Intell Neurosci. 2018 Aug 7;2018:5714872. doi: 10.1155/2018/5714872. eCollection 2018.
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.
本文开发了人工神经网络 (ANN)、自适应神经模糊推理系统 (ANFIS) 模型和基于模糊规则的系统 (FRBS) 模型，以预测欧洲足球比赛的上座需求。为了确定最成功的方法，在不同情况下分析了每种方法。开发了 Elman 反向传播、前馈反向传播和级联前向传播网络类型，以确定表现出色的 ANN 模型。使用反向传播和混合优化方法对模糊推理系统 (FIS) 进行训练，以确定表现出色的 ANFIS 模型。通过实验不同形式的隶属函数来开发模糊逻辑模型。为此，使用 236 场足球比赛的数据来训练 ANN 和 ANFIS 模型，并使用这些俱乐部 2017/2018 赛季的数据来测试所有模型。将所有模型的结果相互比较，并与实际过去的数据进行比较。为了评估每个模型的性能，实施了两个误差度量，即平均绝对百分比误差 (MAPE) 和平均绝对偏差 (MAD)。这些措施表明，具有 Elman 网络类型的 ANN 模型优于其他模型。最后，结果强调提出的 ANN 模型可有效用于预测目的。
