Artificial intelligence approach on predicting current values of polymer interface Schottky diode based on temperature and voltage: An experimental study

Güzel, Tamer; Çolak, Andaç Batur

doi:10.1016/j.spmi.2021.106864

Cited by 33 publications

(23 citation statements)

References 26 publications

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“…In ANN models, the feed-forward (FF) back-propagation (BP) multilayer perceptron (MLP) model is used (Vaferi et al, 2014;C ¸olak, 2020;Canakci et al, 2012;Shafiq et al, 2021a,b). An MLP network has at least one hidden layer, and some networks may have more than one hidden layer (Vaferi et al, 2011;Ahmadloo and Azizi, 2016;Güzel and C ¸olak, 2021). The basic configuration structure of an MLP network model is shown in Fig.…”

Section: Development Of Optimal Ann Modelmentioning

confidence: 99%

Optimization of Darcy-Forchheimer Squeezing Flow in Nonlinear Stratified Fluid Under Convective Conditions With Artificial Neural Network

et al. 2022

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In cases when high velocity occurs, non-Darcy phenomena are essential for explaining fluid motion in porous media and have wide range of applications. The present study displays the magnetohydrodynamic (MHD) squeezing flow of fluid through a non-Darcian medium towards a stretched permeable surface. The heat and mass procedures are investigated using convective conditions and nonlinear stratification. The radiation and viscous dissipation phenomena are implemented to enhance the heat transfer. The nonlinear simplified equations are evaluated using a numerical Runge-Kutta fourth-order approach via the shooting process. To see the variation in the relevant fields, graphs of essential parameters have been provided. The Sherwood number, Nusselt number, and the skin friction coefficient were calculated numerically for various parameters and three different artificial neural networks (ANNs) were developed with the obtained data. The obtained results have shown that artificial neural networks can make predictions and optimizations with high accuracy.

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Section: Development Of Optimal Ann Modelmentioning

confidence: 99%

Optimization of Darcy-Forchheimer Squeezing Flow in Nonlinear Stratified Fluid Under Convective Conditions With Artificial Neural Network

et al. 2022

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“…The next layer is the hidden layer with computational elements called neurons, and each MLP network has at least one hidden layer. [ 41 ] The last layer is the output layer where the prediction values are obtained. Each layer in MLP networks is directly connected to the next layer.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network

Shafiq

Çolak

Swarup

et al. 2022

Advcd Theory and Sims

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The study of reliability analysis of mixture model is essential in confirming the quality of devices, equipment, and electronic tube flops etc. In recent years, statisticians have developed more interest in mixture model research, notably in the last decade, without taking into account the issue of modeling the metrics of reliability of mixture models using artificial neural networks. In the present study, the influence of pertinent parameters on reliability metrics is studied. The effect of components and mixing parameters for failure function, reversed hazard rate function, mean time to failure, hazard rate function, mean inactivity time, mean residual life, reliability function, Mills Ratio profiles are plotted and discussed. A multi‐layer artificial neural network is developed using the numerical analysis results obtained using four different scenarios. The values extracted from the artificial neural network and the numerical findings of the reliability studies are extensively compared and examined. The deviation rates obtained for the developed artificial neural network model are obtained at values lower than 0.12%. The outcomes demonstrate that neural networks are a powerful and effective mathematical tool that can be used in the reliability analysis of mixing models.

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“…In the ANN model, which is designed to estimate the Nu number, the multi-layer perceptron (MLP) network model, which has a reliable estimation capability with its strong architecture, is used [25][26][27][28]. Each layer in the topology of MLP networks is directly coupled to the layer above it [29]. Data is input into the system at the input layer, which is the first layer of an MLP network.…”

Section: Formulation Of Annmentioning

confidence: 99%

Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

2022

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The convective heat transfer in non-Newtonian fluid flow in the presence of temperature stratification, heat generation, and heat absorption effects is debated by using artificial neural networking. The heat transfer rate is examined for the four different thermal flow regimes namely (I) thermal flow field towards a flat surface along with thermal radiations, (II) thermal flow field towards a flat surface without thermal radiations, (III) thermal flow field over a cylindrical surface with thermal radiations, and (IV) thermal flow field over a cylindrical surface without thermal radiations. For each regime, a Nusselt number is carried out to construct an artificial neural networking model. The model prediction performance is reported by using varied neuron numbers and input parameters, and the results are assessed. The ANN model is designed by using the Bayesian regularization training procedure, and a high-performing MLP network model is used. The data used in the creation of the MLP network was 80 percent for model training and 20 percent for testing. The graph shows the degree of agreement between the ANN model projected values and the goal values. We discovered that an artificial neural network model can provide high-efficiency forecasts for heat transfer rates having engineering standpoints. For both flat and cylindrical surfaces, the heat transfer normal to the surface reflects inciting nature towards the Prandtl number and heat absorption parameter, while the opposite is the case for the temperature stratification parameter and heat generation parameter. It is important to note that the magnitude of heat transfer is significantly larger for Flow Regime-IV in comparison with Flow Regimes-I, -II, and -III.

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Artificial intelligence approach on predicting current values of polymer interface Schottky diode based on temperature and voltage: An experimental study

Cited by 33 publications

References 26 publications

Optimization of Darcy-Forchheimer Squeezing Flow in Nonlinear Stratified Fluid Under Convective Conditions With Artificial Neural Network

Optimization of Darcy-Forchheimer Squeezing Flow in Nonlinear Stratified Fluid Under Convective Conditions With Artificial Neural Network

Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network

Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

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