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Proceedings Paper

Simulated annealing and morphology neural networks
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Paper Abstract

Artificial neural networks have proven to be quite useful for a variety of different applications. A recent addition to the arena of neural networks, morphology neural networks use a morphology-like operation as their basic nodal calculation, instead of the usual linear operation. Several morphology neural nets have been developed, and lattice-type learning rules have been used to train these networks. In this paper, we present a different kind of learning rule for morphology neural nets that is based on the simulated annealing algorithm. Simulated annealing has been applied to many different areas involving optimization.

Paper Details

Date Published: 1 June 1992
PDF: 9 pages
Proc. SPIE 1769, Image Algebra and Morphological Image Processing III, (1 June 1992); doi: 10.1117/12.60637
Show Author Affiliations
Jennifer L. Davidson, Iowa State Univ. (United States)

Published in SPIE Proceedings Vol. 1769:
Image Algebra and Morphological Image Processing III
Paul D. Gader; Edward R. Dougherty; Jean C. Serra, Editor(s)

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