An experimental study on determination of the shottky diode current-voltage characteristic depending on temperature with artificial neural network

Çolak, Andaç Batur; Güzel, Tamer; Yıldız, Osman; Özer, Metin

doi:10.1016/j.physb.2021.412852

Cited by 38 publications

(19 citation statements)

References 21 publications

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“…9 depicts the fundamental design of the constructed MLP network. There is no approach for exhibiting the component of computation named neuron in the hidden layer of MLP networks [50] , [51] , [52] . For this reason, the method used in the literature was followed and the performance of neural network models designed having various amounts of neurons was examined.…”

Section: Ann Model Designmentioning

confidence: 99%

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

et al. 2022

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Section: Ann Model Designmentioning

confidence: 99%

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

et al. 2022

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“…There is no mathematical model or a fixed correlation used to determine the number of neurons used in the hidden layer of ANN models. [46,47] For this reason, the performances of the models developed with different neuron numbers were analyzed and the model with five neurons in the hidden layer was used. Levenberg-Marquardt training algorithm, which is frequently used with its high performance, was preferred as the training algorithm in the MLP network model.…”

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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“…Determining the number of neurons is one of the main challenges. The reason for this difficulty is that there is no fixed rule subject to strength of neurons [45,46]. In order to overcome this difficulty, the performance of the network models established with various neuron numbers is evaluated, and the optimum number of neurons is reached [47].…”

Section: Artificial Neural Networking Designmentioning

confidence: 99%

Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles

2022

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For various obstacles in the path of a flowing liquid stream, an artificial neural networking (ANN) model is constructed to study the hydrodynamic force depending on the object. The multilayer perceptron (MLP), back propagation (BP), and feed-forward (FF) network models were employed to create the ANN model, which has a high prediction accuracy and a strong structure. To be more specific, circular-, octagon-, hexagon-, square-, and triangular-shaped cylinders are installed in a rectangular channel. The fluid is flowing from the left wall of the channel by following two velocity profiles explicitly linear velocity and parabolic velocity. The no-slip condition is maintained on the channel upper and bottom walls. The Neumann condition is applied to the outlet. The entire physical design is mathematically regulated using flow equations. The result is presented using the finite element approach, with the LBB-stable finite element pair and a hybrid meshing scheme. The drag coefficient values are calculated by doing line integration around installed obstructions for both linear and parabolic profiles. The values of the drag coefficient are predicted with high accuracy by developing an ANN model toward various obstacles.

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An experimental study on determination of the shottky diode current-voltage characteristic depending on temperature with artificial neural network

Cited by 38 publications

References 21 publications

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

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

Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles

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