An Experimental Study on Artificial Intelligence‐Based Prediction of Capacitance–Voltage Parameters of Polymer‐Interface 6H‐SiC/MEH‐PPV/Al Schottky Diodes

Güzel, Tamer; Çolak, Andaç Batur

doi:10.1002/pssa.202100821

Cited by 11 publications

(10 citation statements)

References 34 publications

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“…The methodology obtained from the literature was used in grouping the data. 36 15% of the data are set aside for model validation, 15% for testing, and 70% for model training. A major challenge is identifying neurons in MLP networks' hidden layers.…”

Section: Ann Modelingmentioning

confidence: 99%

Modeling of Darcy‐Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural network

Shafiq

Çolak

Sindhu

2023

Numerical Methods in Fluids

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Modern industries face a new challenge in cooling processes. Traditional cooling lubricants have limited heatconducting capacity. The development of nanofluids possessing superior properties such as high thermal conductivity, homogeneity, and long‐term stability has revolutionized the cooling lubrication industry. The literature reports a wide range of applications of nanofluid, such as cooling devices, peristaltic pumps for diabetic treatments, accelerators, reactors, petroleum industry applications, solar collectors and so forth. Nanofluids like Williamson nanofluid are very important non‐Newtonian fluids that have pseudoplastic properties. Williamson nanofluid has a number of applications in the medical and engineering sciences. It is used in food processing, inkjet printing, adhesives and emulsions, coated photographic films, and many other applications. In the current study, the nanomaterial flow of the Darcy‐Forchheimer Williamson nanofluid model is evaluated using the Levenberg–Marquardt approach with backpropagated neural networks. Thermalphoresis and Brownian motion are incorporated into the nanofluid model. This system is converted into an analogous nonlinear ordinary differential system through the application of necessary transformations. A dataset for the proposed multilayer perceptron artificial neural network is generated by altering the necessary variables through a Runge–Kutta fourth‐order shooting procedure. It has been created an artificial neural network called a multiple‐layer perceptron in order to forecast the values of the multiple‐layer perceptron. It is discovered that the parameter had the highest mean deviation of and the parameter had the lowest mean deviation of . Furthermore, MSE value of ANN model developed to estimate the skin friction coefficient value as and R value as 0.99954 whereas MSE and R values of the ANN model developed for the estimation of the LNN value were obtained as and 0.99999, respectively.

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Section: Ann Modelingmentioning

confidence: 99%

Modeling of Darcy‐Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural network

Shafiq

Çolak

Sindhu

2023

Numerical Methods in Fluids

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“…For the aforementioned reasons, to analyze the error performance of proposed studies, the calculation of MAE, MSE, RMSE, and R values are frequently used by many researchers. [15,20,23,27,29]…”

Section: Modeling Of I-v Characteristic Of Ni/n-gaas/in Sbdmentioning

confidence: 99%

“…For the proposed approach, MSE and R values are calculated 4.34e‐06 and 0.99728, respectively. [ 27 ] Rabehi et al proposed an approach‐based equilibrium optimizer algorithm for parameter extraction of SBD. The performance of the employed optimization algorithm was compared with those of the Cheung and Kaminski methods.…”

Section: Introductionmentioning

confidence: 99%

Next‐Generation Application‐Based Artificial Intelligence in Modeling and Estimation for Ni/n‐GaAs/In Schottky Barrier Diode

Doğan

Koçkanat

2023

Physica Status Solidi (a)

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Herein, for Ni/n-GaAs/In Schottky barrier diode, experimental measurement, modeling, data generation from the model, and parameter estimation processes are simultaneously carried out. In the experimental step, Ni/n-GaAs/In Schottky barrier diodes are fabricated and annealed from the temperature of 200 °C up to 600 °C with 100 °C steps. Current values are recorded by applying voltage to the diode contacts from À1 V up to 0.5 V. In the modeling step, 1503 experimental current-voltage data are used for 19 different regression models. For Adaptive Neuro Fuzzy System (ANFIS), when root mean square error, mean square error, mean absolute error, and coefficient of determination are calculated 6.0341e-07, 3.6410e-13, 2.3873e-07, and 0.9999 for training, they are obtained 5.8904e-07, 3.4697e-13, 2.3083e-07, and 0.9999 for testing. In the estimation step, the values of electrical parameters are estimated by using Mayfly algorithm. Estimations are performed for all annealing temperatures. In addition, current-voltage data for the annealing temperature of 350 °C are produced by the ANFIS model. Thus, a new-generation artificial intelligence application, that includes measurement, modeling, and estimation for the Ni/n-GaAs/In Schottky barrier diode with varying annealing temperatures, is realized and a new perspective is provided to researchers and practitioners.

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“…MLP networks are one of the rottenly used and highperformance ANN models with their strong structures and working system aiming to minimize the difference between target values and output values. [30][31][32][33] MLP network prototypes fundamentally consist of three layers. The first layer is the input layer where the input parameters are identified.…”

Section: Ann Model Developmentmentioning

confidence: 99%

“…In the designed ANN models, the feed‐forward (FF) backpropagation (BP) multilayer perceptron (MLP) model has been used. MLP networks are one of the rottenly used and high‐performance ANN models with their strong structures and working system aiming to minimize the difference between target values and output values 30–33 . MLP network prototypes fundamentally consist of three layers.…”

Section: Ann Model Developmentmentioning

confidence: 99%

Investigation of flow parameters of Reiner–Philippoff nanofluid flow with higher‐order slip properties, activation energy, and bioconvection by artificial neural networks

Çolak

2022

Heat Trans

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In this study, the flow parameters of Reiner–Philippoff nanofluid flow with high‐order slip properties, activation energy, and bioconvection have been analyzed using artificial neural networks (ANNs). Local Nusselt number (LNN), local Sherwood number (LSN), and motile density number (MDN) are considered as flow parameters. Numerical values have been obtained by numerical methods using flow equations. To estimate the flow parameters, three different ANN models have been designed. The Levenberg–Marquardt training algorithm is used in multilayer perceptron network models with 10 neurons in the hidden layers. In all, 70% of the data set has been used for training the models, 15% for validation, and 15% for testing. The performance analysis of the network models has been made by calculating the determined performance parameters. The R values for the LNN, LSN, and MDN parameters have been calculated as 0.99261, 0.98769, and 0.99102, respectively, and the average deviation values are −0.65%, 0.06%, and −0.11%, respectively. The attained outcomes showed that the ANNs can predict the LNN, LSN, and MDN, which are the flow parameters of the Reiner–Philippoff nanofluid flow, with high accuracy.

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An Experimental Study on Artificial Intelligence‐Based Prediction of Capacitance–Voltage Parameters of Polymer‐Interface 6H‐SiC/MEH‐PPV/Al Schottky Diodes

Cited by 11 publications

References 34 publications

Modeling of Darcy‐Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural network

Modeling of Darcy‐Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural network

Next‐Generation Application‐Based Artificial Intelligence in Modeling and Estimation for Ni/n‐GaAs/In Schottky Barrier Diode

Investigation of flow parameters of Reiner–Philippoff nanofluid flow with higher‐order slip properties, activation energy, and bioconvection by artificial neural networks

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