An “intelligent” approach based on side-by-side cascade-correlation neural networks for estimating thermophysical properties from photothermal responses - Archive ouverte HAL Access content directly
Journal Articles European Physical Journal: Applied Physics Year : 2015

An “intelligent” approach based on side-by-side cascade-correlation neural networks for estimating thermophysical properties from photothermal responses

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Abstract

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.

Dates and versions

hal-01175565 , version 1 (10-07-2015)

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Stéphane Grieu, Olivier Faugeroux, Adama Traoré, Bernard Claudet, Jean-Luc Bodnar. An “intelligent” approach based on side-by-side cascade-correlation neural networks for estimating thermophysical properties from photothermal responses. European Physical Journal: Applied Physics, 2015, 69 (1), 8 p. ⟨10.1051/epjap/2014140254⟩. ⟨hal-01175565⟩
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