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Automatic target detection using entropy optimized shared-weight neural networks

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2 Author(s)
M. A. Khabou ; Dept. of Comput. Eng. & Comput. Sci., Missouri Univ., Columbia, MO, USA ; P. D. Gader

Standard shared-weight neural networks previously demonstrated inferior performance to that of morphological shared-weight neural networks for automatic target detection. Empirical analysis showed that entropy measures of the features generated by the standard shared-weight neural networks were consistently lower than those generated by the morphological shared-weight neural networks. Based on this observation, an entropy maximization term was added to the standard shared-weight network objective function. In this paper, we present automatic target detection results for standard shared-weight neural networks trained with and without the added entropy term

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IEEE Transactions on Neural Networks  (Volume:11 ,  Issue: 1 )