Automatic Target Detection Using Entropy Optimized Shared-Weight Neural Networks  

Oleh:

Khabou, M. A. ; Gader, P. D.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 1 (2000), page 186-193.
Topik: NEURAL NETWORKS; automatic; target deetction; entropy; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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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