Estimation of Boundary Conditions in Conduction Heat Transfer by Neural Networks

E.H. SHIGUEMORI, F.P. HARTER, H.F. CAMPOS VELHO, J.D.S. da SILVA

Abstract


Two different artificial neural networks (NN) are used for estimating a time dependent boundary condition (x = 0) in a slab: multilayer perceptron (MP) and radial base function (RBF). The input for the NN is the temperature time-series obtained from a probe next to boundary of interest. Our numerical experiments follow the work of Krejsa et al. [4]. The NNs were trainned considering 5 per cent of noise in the experimental data. The training was performed considering 500 similar test-functions and 500 different test-functions. Inversions with trained NNs with different test-functions were better. The RBF-NN presented a slightly better results than MP-NN.

References


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DOI: https://doi.org/10.5540/tema.2002.03.02.0189

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Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)

 

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