Entropy optimized morphological shared-weight neural networks

Morphological shared-weight neural networks previously demonstrated perform ance superior to that of MACE fitters and standard shared-weight neural net works 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 observat ion, an entropy maximization term was added to the morphological shared-wei ght network objective function. In this paper, target detection results are presented for morphological shared-weight networks trained with and withou t entropy terms. (C) 1999 Society of Photo-Optical Instrumentation Engineer s. [S0091-3286(99)00502-4].