Article ID Journal Published Year Pages File Type
663367 International Journal of Heat and Mass Transfer 2006 19 Pages PDF
Abstract
This paper employs the continuous-time analogue Hopfield neural network to compute the temperature distribution in forward heat conduction problems and solves inverse heat conduction problems by using a back propagation neural (BPN) network to identify the unknown boundary conditions. The weak generalization capacity of BPN networks is improved by employing the Bayesian regularization algorithm. The feasibility of the proposed method is examined in a series of numerical simulations. The results show that the proposed neural network analysis method successfully solves forward heat conduction problems and is capable of predicting the unknown parameters in inverse problems with an acceptable error.
Keywords
Related Topics
Physical Sciences and Engineering Chemical Engineering Fluid Flow and Transfer Processes
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