Control of Grid-tied Dual-PV LLC Converter using Adaptive Neuro Fuzzy Interface System (ANFIS)

2021 ◽  
Author(s):  
Sumana Ghosh ◽  
Abdullah Alhatlani ◽  
Reza Rezaii ◽  
Issa Batarseh
Keyword(s):  
Neuro Fuzzy ◽  
Interface System

A COMPARATIVE STUDY OF MULTIPLE REGRESSION AND MACHINE LEARNING TECHNIQUES FOR PREDICTION OF NANOFLUID HEAT TRANSFER

2021 ◽  
pp. 1-22
Author(s):  
Eyup Kocak ◽  
Ulku Ece Ayli ◽  
Hasmet Turkoglu
Keyword(s):  
Heat Transfer ◽  
Nusselt Number ◽  
Multiple Regression ◽  
Volume Fraction ◽  
Particle Diameter ◽  
Machine Learning Techniques ◽  
Exponential Factor ◽  
Nano Fluid ◽  
Neuro Fuzzy ◽  
Interface System

Abstract The aim of this paper is to introduce and discuss prediction power of the multiple regression technique, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods for predicting the forced convection heat transfer characteristics of a turbulent nano fluid flow a pipe. Water and Al2O3 mixture is used as the nano fluid. Utilizing FLUENT software, numerical computations were performed with volume fraction ranging between 0.3% and 5%, particle diameter ranging between 20 and 140 nm and Reynolds number ranging between 7000 and 21000. Based on the computationally obtained results, a correlation is developed for Nusselt number using the multiple regression method. Also, based on the CFD results different ANN architectures with different number of neurons in the hidden layers and several training algorithms (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient) are tested to find the best ANN architecture. In addition, Adaptive Neuro-fuzzy Interface System (ANFIS) is also used to predict the Nusselt number. In the ANFIS, number of clusters, exponential factor and Membership Function (MF) type are optimized. The results obtained from multiple regression correlation, ANN and ANFIS were compared. According to the obtained results, ANFIS is a powerful tool with a R2 of 0.9987 for predictions.

scholarly journals Identification of Sine, Squire, Triangle and Sawtooth Waveforms with Uniform White and Inverse F Noises by Adaptive Neuro - Fuzzy Interface System

2018 ◽  
Vol 11 (3) ◽  
pp. 128-132
Author(s):  
G. Georgiev ◽  
◽  
I. Balabanova ◽  
P. Kogias ◽  
S. Sadinov ◽  
...  
Keyword(s):  
Neuro Fuzzy ◽  
Interface System

Modeling of mixed convection in an enclosure using multiple regression, artificial neural network, and adaptive neuro-fuzzy interface system models

2020 ◽  
Vol 234 (15) ◽  
pp. 3078-3093
Author(s):  
Ece Aylı
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Nusselt Number ◽  
Forced Convection ◽  
Number Distribution ◽  
Neuro Fuzzy ◽  
Interface System ◽  
Nusselt Number Distribution ◽  
Artificial Neural

In this study, the heat transfer characteristics of laminar combined forced convection through a horizontal duct are obtained with the help of the numerical methods. The effect of the geometrical parameters of the cavity and Reynolds number on the heat transfer is investigated. New heat transfer correlation for hydrodynamically fully developed, laminar combined forced convection through a horizontal duct is proposed with an average error of 6.98% and R2 of 0.8625. The obtained correlation results are compared with the artificial neural network and adaptive neuro-fuzzy interface system models. Due to the obtained results, good agreement is identified between the numerical results and predicted adaptive neuro-fuzzy interface system results. In conclusion, it is seen that adaptive neuro-fuzzy interface system can predict the Nusselt number distribution with a higher accuracy than the developed correlation and the artificial neural network model. The developed adaptive neuro-fuzzy interface system model predicts the Nusselt number with 1.07% mean average percentage error and 0.9983 R2 value. The effect of the different training algorithms and their ability to predict Nusselt number distribution are examined. According to the results, the Bayesian regulation algorithm gives the best approach with a 2.235% error. According to the examination that is performed in this study, the adaptive neuro-fuzzy interface system is a powerful, robust tool that can be used with confidence for predicting the thermal performance.

Non-intrusive Load Monitoring for Residential Customers Using Adaptive-Neuro Fuzzy Interface System and Fine Tree Classifier

2020 ◽  
Author(s):  
Muhammad Zaigham Abbas ◽  
Intisar Ali Sajjad ◽  
Rehan Liaqat ◽  
Muhammad Abdullah ◽  
Muhammad Athar Shah ◽  
...  
Keyword(s):  
Neuro Fuzzy ◽  
Tree Classifier ◽  
Interface System ◽  
Load Monitoring

scholarly journals Measuring and Comparing Forces Acting on Moldboard Plow and Para-Plow with Wing to Replace Moldboard Plow with Para-Plow for Tillage and Modeling It Using Adaptive Neuro-Fuzzy Interface System (ANFIS)

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 633
Author(s):  
Mohammadreza Abbaspour-Gilandeh ◽  
Gholamhossein Shahgoli ◽  
Yousef Abbaspour-Gilandeh ◽  
Miguel Apolonio Herrera-Miranda ◽  
José Luis Hernández-Hernández ◽  
...  
Keyword(s):  
Neural System ◽  
Effective Parameters ◽  
Lateral Forces ◽  
Neuro Fuzzy ◽  
Interface System ◽  
Fuzzy Neural ◽  
Draft Force ◽  
Loam Soil ◽  
Moldboard Plow ◽  
Traveling Speed

The objective of this study was to measure the draft, vertical, and lateral forces acting on the moldboard plow, para-plow without a wing, para-plow with forward-bent wing, and para-plow with a backward-bent wing at three working depths and three forward speeds in clay loam soil to investigate the use of a suitable para-plow instead of the moldboard plow. Also, modeling the draft, vertical, and lateral forces acting on the implements using Adaptive Neuro-Fuzzy Interface System (ANFIS) was another objective of this research. To measure the draft, vertical, and lateral forces, a three-point hitch dynamometer was used. The results showed that, with the increment of the forward speed and working depth, the draft force required by the used implements increased. This increase was also true for vertical and lateral forces acting on the implements. Modeling of the draft, vertical, and lateral forces acting on the implements was performed using the effective parameters of the implements, working depth, and forward traveling speed using the (ANFIS) fuzzy neural system model. The root mean square error (RMSE) for the draft, vertical, and lateral forces for the above models were obtained equal to 0.121, 0.014, and 0.016, respectively.

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