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

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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Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

Open Life Sciences ◽

10.2478/s11535-014-0356-4 ◽

2014 ◽

Vol 9 (12) ◽

pp. 1226-1234

Author(s):

Kadir Temizel ◽

Mehmet Odabas ◽

Nurettin Senyer ◽

Gokhan Kayhan ◽

Sreekala Bajwa ◽

...

Keyword(s):

High Performance ◽

Fuzzy Inference ◽

High Water ◽

Stress Conditions ◽

Ann Model ◽

Inference System ◽

Leaf Reflectance ◽

Neuro Fuzzy ◽

Salinity Levels ◽

Artificial Neural Network Ann

AbstractLack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

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Intelligent Systems for Structural Damage Assessment

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0193 ◽

2018 ◽

Vol 29 (1) ◽

pp. 378-392

Author(s):

Eleni Vrochidou ◽

Petros-Fotios Alvanitopoulos ◽

Ioannis Andreadis ◽

Anaxagoras Elenas

Keyword(s):

Damage Assessment ◽

Intelligent Systems ◽

Structural Damage ◽

Fuzzy Inference ◽

Damage Index ◽

Reinforced Concrete Structure ◽

Ann Model ◽

Inference System ◽

Time Frequency ◽

Artificial Neural Network Ann

Abstract This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio (MISDR). Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different techniques for the set of the proposed seismic features: an artificial neural network (ANN), a Mamdani-type fuzzy inference system (FIS), and a Sugeno-type FIS. The performance of the models is evaluated in terms of the mean square error (MSE) between the actual calculated and estimated MISDR values derived from the proposed models. All models provide small MSE values. Yet, the ANN model reveals a slightly better performance.

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ANFIS-based approach for the estimation of transverse mixing coefficient

Water Science & Technology ◽

10.2166/wst.2011.282 ◽

2011 ◽

Vol 63 (5) ◽

pp. 1004-1009 ◽

Cited By ~ 13

Author(s):

Z. Ahmad ◽

H. Md. Azamathulla ◽

N. A. Zakaria

Keyword(s):

Fuzzy Inference ◽

Near Field ◽

Shear Velocity ◽

Effective Control ◽

Ann Model ◽

Inference System ◽

Transverse Mixing ◽

Mixing Coefficient ◽

Wide Range ◽

Artificial Neural Network Ann

Understanding of the fate of pollutants, disposed of in streams, is a matter of concern in recent years for the effective control of pollution. Transverse mixing of the pollutants in open channels is arguably more important than the longitudinal mixing and near-field mixing. Several attempts have been made to establish the relationship between the transverse mixing coefficient and bulk channel and flow parameters such as width, depth, shear velocity, friction factor, curvature and sinuosity. This paper presents adaptive neuro fuzzy inference system (ANFIS) approach to predict the transverse mixing coefficient in open channel flows. Available laboratory and field data for the transverse mixing coefficients covering wide range of channel and flow conditions are used for the development and testing of the proposed method. The proposed ANFIS approach produces satisfactory results (R2=0.945) compared to the artificial neural network (ANN) model and existing predictors for mixing coefficient.

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Rain prediction using fuzzy rule based system in North-West malaysia

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v14.i3.pp1564-1573 ◽

2019 ◽

Vol 14 (3) ◽

pp. 1564

Author(s):

Noor Zuraidin Mohd Safar ◽

Azizul Azhar Ramli ◽

Hirulnizam Mahdin ◽

David Ndzi ◽

Ku Muhammad Naim Ku Khalif

Keyword(s):

Fuzzy Logic ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Expert Knowledge ◽

Fuzzy Rule ◽

Complex Problem ◽

Dew Point ◽

Inference System ◽

North West ◽

Rainfall Prediction

The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed. Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%

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Algorithm Apriori Association Rule in Determination of Fuzzy Rule Based on Comparison of Fuzzy Inference System (FIS) Mamdani Method and Sugeno Method

Business Management and Strategy ◽

10.5296/bms.v7i1.9410 ◽

2016 ◽

Vol 7 (1) ◽

pp. 103

Author(s):

Muhammad Fadli Arif ◽

Bima Anoraga ◽

Samingun Handoyo ◽

Harisaweni Nasir

Keyword(s):

Fuzzy Logic ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Expert Knowledge ◽

Fuzzy Rule ◽

Percentage Error ◽

Inference System ◽

Rule Based ◽

Accuracy Measurement ◽

First Order

The economic stability of a country can be determined from the changes in the rate of inflation. Inflation is measured by the annual percentage change in the Consumer Price Index. Since there exists some uncertainties in the inflation data, fuzzy logic is one of the ways to analyse the data. Decisions in fuzzy logic can be made using the fuzzy rule-based inference system. Fuzzy rule-based inference can be obtained from expert knowledge, but the knowledge from the experts on the working of a system is not always available. Therefore, the use of association rules approach could solve the problem. Using three methods of fuzzy inferences; namely the Mamdani Methods, zero-order Sugeno method, and the first-order Sugeno method, this study was carried out to determine which method fits to predict the general monthly inflation data in Indonesia. The Inflation data were derived from the inflation of foodstuff price, X1; inflation of food, drinks, cigarettes and tobacco prices, X2; inflation of housing, water, electricity, gas, and fuel prices, X3; inflation of clothing price, X4; inflation of health care price, X5; inflation of education, recreation, and sports prices, X6; and inflation of transportation, communication, and financial services prices, X7. The performance of the three methods was compared using mean squared error (MSE) and mean absolute percentage error (MAPE) as the accuracy measurement to establish the best fuzzy inference method that fits the inflation value. It was found that the most appropriate method which generated the most accurate results to fit the fuzzy inference system to the inflation data was the first-order Sugeno method.

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Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

The Scientific World JOURNAL ◽

10.1155/2015/742138 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 11

Author(s):

Nevenka Djurovic ◽

Milka Domazet ◽

Ruzica Stricevic ◽

Vesna Pocuca ◽

Velibor Spalevic ◽

...

Keyword(s):

Water Table ◽

Groundwater Resources ◽

Danube River ◽

Left Bank ◽

Ann Model ◽

Inference System ◽

Soft Computing Techniques ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Adaptive Neurofuzzy Inference System

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

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An Intelligent System for Load Forecasting on a Distribution Network

Journal of Digital Food, Energy & Water Systems ◽

10.36615/digitalfoodenergywatersystems.v2i1.550 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Saheed Gbadamosi ◽

Ojo Olukayode Adedayo

Keyword(s):

Fuzzy Logic ◽

Fuzzy Inference ◽

Intelligent System ◽

Distribution Network ◽

Load Forecasting ◽

Integrated System ◽

Fuzzy Logic Systems ◽

Inference System ◽

Load Forecast ◽

Artificial Neural Network Ann

This paper presents an Adaptive Neuro-Fuzzy Inference system (ANFIS) technique for medium-term load forecasting in a distribution network. This technique is an integrated system consisting of fuzzy logic systems and Artificial Neural network (ANN). The inputs to the system include days of the week, temperature, time, current and previous hourly load on the distribution network. The data collection is within the period of two years. The formulation and mapping of the input data is done using fuzzy logic system and ANN are employed for generation of inference. The experimental results show the average load pre-diction accuracy of 87.23% and regression coefficient of 0.873. The analysis of the proposed ANFIS for load forecast is effective in planning, managing and organizing the electric load forecast with accurate prediction.

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Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽

10.3390/sym11020286 ◽

2019 ◽

Vol 11 (2) ◽

pp. 286 ◽

Cited By ~ 8

Author(s):

Athanasios Bogiatzis ◽

Basil Papadopoulos

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Initial Step ◽

Local Area ◽

Mathematical Functions ◽

Inference System ◽

Neuro Fuzzy ◽

Entropy Measures ◽

Complex Procedure ◽

Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

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Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

Journal of Marine Science and Engineering ◽

10.3390/jmse9010049 ◽

2021 ◽

Vol 9 (1) ◽

pp. 49

Author(s):

Tanja Brcko ◽

Andrej Androjna ◽

Jure Srše ◽

Renata Boć

Keyword(s):

Fuzzy Logic ◽

Collision Avoidance ◽

Decision Model ◽

Fuzzy Inference ◽

Weather Conditions ◽

Target Vessel ◽

Planning Tool ◽

Inference System ◽

International Regulations ◽

Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

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A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

Advances in Fuzzy Systems ◽

10.1155/2013/343174 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Arati M. Dixit ◽

Harpreet Singh

Keyword(s):

Fuzzy Logic ◽

Soft Computing ◽

Fuzzy Inference System ◽

Source Identification ◽

Field Programmable Gate Array ◽

Crack Detection ◽

Fuzzy Inference ◽

Inference System ◽

Field Programmable ◽

Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

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