An ANFIS based method of agent behavior in simulated soccer agents

Zafarani, Reza; Yazdchi, Mohammadreza; Salehi, Seyedeh Akram

doi:10.1109/ictai.2005.32

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…The ANFIS has been successfully implemented to a broad range of problems in a variety of fields including economics [ 31 ], transportation [ 32 ], energy [ 33 ], health [ 34 ] and the environment [ 35 ] for different purposes including diagnosis [ 34 ], evaluation [ 36 ], prediction [ 37 ], and forecasting [ 38 ]. However, it is in infancy in sports in which it has been used for simulated soccer agents [ 39 ] and forecasting attendance of soccer games recently [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

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

Şahin

Erol

2018

Computational Intelligence and Neuroscience

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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.

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Section: Introductionmentioning

confidence: 99%

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

Şahin

Erol

2018

Computational Intelligence and Neuroscience

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“…The ANFIS has been applied to many areas including economics [31], passenger demand forecasting [32], energy [33], and the environment [34]. However, it has been underused in sports, having only been used for simulated soccer agents [35].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Şahin

Erol

2017

MCA

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Abstract:The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

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Modeling Soil Water Retention Under Different Pressures Using Adaptive Neuro-Fuzzy Inference System

Elbeltagi

Jaiswal

Galkate

et al. 2023

Water Resour Manage

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Soil Water Retention (SWR) is an important process in drainage, surface, and groundwater partitioning, hydrological modeling, water supply for irrigation, etc. Assessment of SWR characteristics is complex and difficult to conduct spatially in varied locations. Therefore, Pedotransfer Functions (PTF) which are empirical relations with easily available physical properties are commonly used. In the present study, the evaluation of soil moisture at different suction pressure using the adaptive neuro-fuzzy inference systems (ANFIS) approach based on soil texture (percentage of gravel, sand, silt, and clay) and compare with the PTF approach. The analysis was conducted for a total of eleven sites of two adjoining commands in India. The pressure plate apparatus along with coarse and fine sieve analysis, titration, and other tests were carried out to determine SWR, texture, organic carbon, and bulk density. The comparative analysis of Nash–Sutcliffe efficiencies of the best-fitted PTF models and ANFIS model confirmed that the ANFIS model can capture all variations of soil texture across all sites with Nash–Sutcliffe efficiency of nearly 1.0 indicative of an exact match, while no single PTF-based model can be used for all the sites. Therefore, the ANFIS model can be used to model soil water retention for the central India region using easily available texture properties of soils.

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An ANFIS based method of agent behavior in simulated soccer agents

Cited by 3 publications

References 5 publications

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

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

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Modeling Soil Water Retention Under Different Pressures Using Adaptive Neuro-Fuzzy Inference System

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