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Proceedings Paper

Configuring artificial neural networks to implement function optimization
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Paper Abstract

Threshold binary networks of the discrete Hopfield-type lead to the efficient retrieval of the regularized least-squares (LS) solution in certain inverse problem formulations. Partitions of these networks are identified based on forms of representation of the data. The objective criterion is optimized using sequential and parallel updates on these partitions. The algorithms consist of minimizing a suboptimal objective criterion in the currently active partition. Once the local minima is attained, an inactive partition is chosen to continue the minimization. This strategy is especially effective when substantial data must be processed by resources which are constrained either in space or available bandwidth.

Paper Details

Date Published: 5 April 2002
PDF: 9 pages
Proc. SPIE 4668, Applications of Artificial Neural Networks in Image Processing VII, (5 April 2002); doi: 10.1117/12.461679
Show Author Affiliations
Ramakrishnan Sundaram, Gannon Univ. (United States)

Published in SPIE Proceedings Vol. 4668:
Applications of Artificial Neural Networks in Image Processing VII
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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