An edge detector based on artificial neural network with application to hybrid Compact-WENO finite difference schemes

A new approach is proposed to detect edges based on an articial neural network (ANN). Some elementary continuous and discontinuous functions interpolated in the polynomial space and their continuity are used as the training sets to train a back propagation neural network containing two hidden layers. The ANN edge detector is used to detect the edges in an image and the locations of discontinuity in the hybrid fth order Compact-WENO nonlinear (Hybrid)scheme for solving hyperbolic conservation laws with solutions containing both discontinuous and complex ne scale structures. Several classical examples in the image processing show that ANN edge detector can capture an edge accurately with fewer grid points than the classical multi-resolution (MR) analysis. Furthermore, as oppose to the MR analysis, ANN edge detector is robust with no problem dependent parameter, in addition to being accurate and ecient. The performance of the Hybrid scheme with the ANN edge detector is demonstrated with several one and two-dimensional benchmark examples in shallow water equations and Euler equations.

Oct 20 2019

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 Record created 2019-10-20, last modified 2019-12-05

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