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• W2547020512 abstract "A methodology is proposed to estimate sea surface currents from the information given by the sea surface temperature (SST) obtained from satellite images. Currents are estimated from the motion field of a temporal sequence of images using a Hopfield neural network. A cost function is minimized when some rules of correspondence between pixels in successive images are considered. A performance test of their methodology is done using the information from the paths made by Lagrangian buoys. The simulations, done using the buoys positions, prevents the use of a system able to escape from the local minima, where the Hopfield neural network becomes trapped, until reaching a global minima." @default.
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• W2547020512 date "2002-11-27" @default.
• W2547020512 modified "2023-09-26" @default.
• W2547020512 title "A Hopfield neural network to track drifting buoys in the ocean" @default.
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• W2547020512 doi "https://doi.org/10.1109/oceans.1998.724389" @default.
• W2547020512 hasPublicationYear "2002" @default.
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