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Proceedings Paper

Fast analog associative memory
Author(s): Jason M. Kinser
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Paper Abstract

Traditional neural networks such as backpropagation begin with a set of decision boundaries and optimize the network by moving the boundaries. The problem with this approach is a large number of iterations is required and the network can easily be stuck in a local minima. The algorithm presented here rapidly creates boundaries when necessary and destroys boundaries when they become obsolete. Optimization is achieved by a 'survival of the fittest' boundaries approach. Since the individual boundaries are not optimized the algorithm does not require iterations and trains the network very quickly. The algorithm is well suited for high- dimensional analog inputs and analog outputs.

Paper Details

Date Published: 11 August 1995
PDF: 4 pages
Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); doi: 10.1117/12.216362
Show Author Affiliations
Jason M. Kinser, Alabama A&M Univ. (United States)


Published in SPIE Proceedings Vol. 2568:
Neural, Morphological, and Stochastic Methods in Image and Signal Processing
Edward R. Dougherty; Francoise J. Preteux; Sylvia S. Shen, Editor(s)

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