Next Article in Journal
Area Efficient Dual-Fed CMOS Distributed Power Amplifier
Previous Article in Journal
A Pipelined FFT Processor Using an Optimal Hybrid Rotation Scheme for Complex Multiplication: Design, FPGA Implementation and Analysis
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Electronics 2018, 7(8), 138; https://doi.org/10.3390/electronics7080138

Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

1
Department of Electrical Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan
2
Department of Electrical Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22040, Pakistan
3
Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan
4
Department of Computer Science & IT, Ghazi University, D.G. Khan 32200, Pakistan
*
Author to whom correspondence should be addressed.
Received: 13 July 2018 / Revised: 31 July 2018 / Accepted: 1 August 2018 / Published: 3 August 2018
Full-Text   |   PDF [2193 KB, uploaded 8 August 2018]   |  

Abstract

Contemplating the importance of studying current–voltage curves in superconductivity, it has been recently and rightly argued that their approximation, rather than incessant measurements, seems to be a more viable option. This especially becomes bona fide when the latter needs to be recorded for a wide range of critical parameters including temperature and magnetic field, thereby becoming a tedious monotonous procedure. Artificial neural networks have been recently put forth as one methodology for approximating these so-called electrical measurements for various geometries of antidots on a superconducting thin film. In this work, we demonstrate that the prediction accuracy, in terms of mean-squared error, achieved by artificial neural networks is rather constrained, and, due to their immense credence on randomly generated networks’ coefficients, they may result in vastly varying prediction accuracies for different geometries, experimental conditions, and their own tunable parameters. This inconsistency in prediction accuracies is resolved by controlling the uncertainty in networks’ initialization and coefficients’ generation by means of a novel entropy based genetic algorithm. The proposed method helps in achieving a substantial improvement and consistency in the prediction accuracy of current–voltage curves in comparison to existing works, and is amenable to various geometries of antidots, including rectangular, square, honeycomb, and kagome, on a superconducting thin film. View Full-Text
Keywords: superconducting film; Shapiro steps; artificial neural networks; genetic algorithms; entropy superconducting film; Shapiro steps; artificial neural networks; genetic algorithms; entropy
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Naqvi, S.R.; Akram, T.; Haider, S.A.; Kamran, M.; Shahzad, A.; Khan, W.; Iqbal, T.; Umer, H.G. Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films. Electronics 2018, 7, 138.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top