Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2016 (2016), Article ID 1709827, 7 pages
http://dx.doi.org/10.1155/2016/1709827
Research Article

Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB2 Superconductors Using Room Temperature Resistivity

1Physics Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
2Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, Ondo State 342111, Nigeria
3Institute for Digital Communications, School of Engineering, University of Edinburgh, UK
4Computer Information Systems Department, University of Dammam, Dammam 31451, Saudi Arabia

Received 22 November 2015; Revised 15 April 2016; Accepted 24 April 2016

Academic Editor: Sebastian Ventura

Copyright © 2016 Taoreed O. Owolabi et al. 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.

Abstract

Doping and fabrication conditions bring about disorder in MgB2 superconductor and further influence its room temperature resistivity as well as its superconducting transition temperature (). Existence of a model that directly estimates of any doped MgB2 superconductor from the room temperature resistivity would have immense significance since room temperature resistivity is easily measured using conventional resistivity measuring instrument and the experimental measurement of wastes valuable resources and is confined to low temperature regime. This work develops a model, superconducting transition temperature estimator (STTE), that directly estimates of disordered MgB2 superconductors using room temperature resistivity as input to the model. STTE was developed through training and testing support vector regression (SVR) with ten experimental values of room temperature resistivity and their corresponding using the best performance parameters obtained through test-set cross validation optimization technique. The developed STTE was used to estimate of different disordered MgB2 superconductors and the obtained results show excellent agreement with the reported experimental data. STTE can therefore be incorporated into resistivity measuring instruments for quick and direct estimation of of disordered MgB2 superconductors with high degree of accuracy.