Estimation of Boundary Conditions in Conduction Heat Transfer by Neural Networks
DOI:
https://doi.org/10.5540/tema.2002.03.02.0189Abstract
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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