Eur. Phys. J. Appl. Phys.
Volume 69, Number 1, January 2015
|Number of page(s)||8|
|Section||Instrumentation and Metrology|
|Published online||07 January 2015|
An “intelligent” approach based on side-by-side cascade-correlation neural networks for estimating thermophysical properties from photothermal responses
PROMES, Université de Perpignan Via Domitia, 52 avenue Paul Alduy, 66860
2 GRESPI/ECATHERM, Université de Reims Champagne-Ardenne, BP 1039, 51687 Reims, France
a e-mail: firstname.lastname@example.org
Revised: 9 October 2014
Accepted: 2 December 2014
Published online: 7 January 2015
In the present paper, an artificial-intelligence-based approach dealing with the estimation of thermophysical properties is designed and evaluated. This new and “intelligent” approach makes use of photothermal responses obtained when subjecting materials to a light flux. So, the main objective of the present work was to estimate simultaneously both the thermal diffusivity and conductivity of materials, from front-face or rear-face photothermal responses to pseudo random binary signals. To this end, we used side-by-side feedforward neural networks trained with the cascade-correlation algorithm. In addition, computation time was a key point to consider. That is why the developed algorithms are computationally tractable.
© EDP Sciences, 2015
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