Entropy optimized morphological shared-weight neural networks

Morphological shared-weight neural networks previously demonstrated performance superior to that of MACE fitters and standard shared-weight neural networks for target detection. Empirical analysis showed that entropy measures of the morphological shared-weight networks were consistently higher than those of the standard shared-weight neural networks. Based on this observation, an entropy maximization term was added to the morphological shared-weight network objective function. In this paper, target detection results are presented for morphological shared-weight networks trained with and without entropy terms. (C) 1999 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(99)00502-4].