scholarly journals A new innovative method for model efficiency performance

2021 ◽  
Author(s):  
Zekâi Şen ◽  
Eyüp Şişman ◽  
Burak Kızılöz
Keyword(s):  
Fuzzy Inference ◽  
Statistical Efficiency ◽  
Inference System ◽  
Efficiency Assessment ◽  
Visual Science ◽  
Model Predictions ◽  
Fitting Procedures ◽  
Efficiency Performance ◽  
Model Efficiency ◽  
Artificial Neural Network Ann

Abstract In every aspect of scientific research, model predictions need calibration and validation as their representativity of the record measurement. In the literature, there are a myriad of formulations, empirical expressions, algorithms and software for model efficiency assessment. In general, model predictions are curve fitting procedures with a set of assumptions that are not cared for sensitively in many studies, but only a single value comparison between the measurements and predictions is taken into consideration, and then the researcher makes the decision as for the model efficiency. Among the classical statistical efficiency formulations, the most widely used ones are bias (BI), mean square error (MSE), correlation coefficient (CC) and Nash-Sutcliffe efficiency (NSE) procedures all of which are embedded within the visual inspection and numerical analysis (VINAM) square graph as measurements versus predictions scatter diagram. The VINAM provides a set of verbal interpretations and then numerical improvements embracing all the previous statistical efficiency formulations. The fundamental criterion in the VINAM is 1:1 (45°) main diagonal along which all visual, science philosophical, logical, rational and mathematical procedures boil down for model validation. The application of the VINAM approach is presented for artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) model predictions.

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

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
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.

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

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.

Characteristic of Fuzzy, ANN, and ANFIS for Brushless DC Motor Controller: An Evaluation by Dynamic Test

2021 ◽  
Vol 13 (6) ◽  
Author(s):  
◽  
Andi Setiawan ◽  
Bayu Rudiyanto ◽  
Satryo Budi Utomo ◽  
Muji Muji Setiyo ◽  
...  
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Dynamic Testing ◽  
Dynamic Test ◽  
Absolute Error ◽  
Inference System ◽  
Brushless Dc ◽  
Average Value ◽  
Constant Torque ◽  
Artificial Neural Network Ann

Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the BLDC control system in previous studies was compared separately with their respective parameters, making it difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy, ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests are conducted to investigate control performance under constant torque-various speed and constant speed-various torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289 e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS. Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS).

Intelligent Systems for Structural Damage Assessment

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.

Investigation of trap efficiency of retention dams

2017 ◽  
Vol 18 (2) ◽  
pp. 450-459 ◽  
Author(s):  
Abbas Parsaie ◽  
Samad Ememgholizadeh ◽  
Amir Hamzeh Haghiabi ◽  
Amir Moradinejad
Keyword(s):  
Fuzzy Inference ◽  
Kernel Functions ◽  
Support Vector ◽  
Inference System ◽  
Trap Efficiency ◽  
Neuro Fuzzy ◽  
Management Plans ◽  
Longitudinal Slope ◽  
The Mean ◽  
Artificial Neural Network Ann

Abstract In this paper, the trap efficiency (TE) of retention dams was investigated using laboratory experiments. To map the relation between TE and involved parameters, artificial intelligence (AI) methods including artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were utilized. Results of experiments indicated that the range of TE varies between 30 and 98%; hence, this structure can be recommended to control sediment transport in watershed management plans. Experimental results showed that by increasing the longitudinal slope of streams, TE decreases. This finding was observed for Vf/Vs parameter, as well. By increasing the mean diameter grain size (D50) and specific gravity of sediments (Gs), TE increases. Results of all applied AI models demonstrated that all of them have suitable performance; however, the minimum data dispersivity was observed in SVM outcomes. It is notable that the best performance of transfer, membership and kernel functions were related to tansig, gaussmf and radial basis function (RBF) for ANN, SVM and ANFIS, respectively.

scholarly journals Specific Wear Rate Modeling of Polytetraflouroethylene Composites Via Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) Tools

2021 ◽  
Author(s):  
Musa Alhaji Ibrahim ◽  
Yusuf Şahin ◽  
Auwal Ibrahim ◽  
Auwalu Yusuf Gidado ◽  
Mukhtar Nuhu Yahya
Keyword(s):  
Neural Network ◽  
Wear Rate ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Specific Wear Rate ◽  
Inference System ◽  
Modeling And Prediction ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Ptfe Composites

Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites.

scholarly journals Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3628 ◽  
Author(s):  
Ibrahim M. Alarifi ◽  
Hoang M. Nguyen ◽  
Ali Naderi Bakhtiyari ◽  
Amin Asadi
Keyword(s):  
Thermal Conductivity ◽  
Thermophysical Properties ◽  
Fuzzy Inference ◽  
Optimization Techniques ◽  
Hybrid Nanofluid ◽  
Inference System ◽  
Laminar And Turbulent Flow ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Thermal Oil

The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.

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