Estimation of Boundary Conditions in Conduction Heat Transfer by Neural Networks

Authors

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

DOI:

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

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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Published

2002-06-01

How to Cite

SHIGUEMORI, E., HARTER, F., CAMPOS VELHO, H., & da SILVA, J. (2002). Estimation of Boundary Conditions in Conduction Heat Transfer by Neural Networks. Trends in Computational and Applied Mathematics, 3(2), 189–195. https://doi.org/10.5540/tema.2002.03.02.0189

Issue

Vol. 3 No. 2 (2002)

Section

Original Article

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