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Further noise rejection in linear associative memories

โœ Scribed by Sheng-Wei Zhang; A.G. Constantinides; Li-He Zou

Book ID
104348508
Publisher
Elsevier Science
Year
1992
Tongue
English
Weight
390 KB
Volume
5
Category
Article
ISSN
0893-6080

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โœฆ Synopsis

Abstraet--lt is well known that linear associative memories are sensitive to input noise. In this paper, a new noise rejection method for linear associative memories is proposed. It is shown that with a little cost in recall bias the model is much more resistant to noise than previous models, especially as the number of stored vector pairs approaches the number of the key vector components. Using the singular value decomposition (SVD) of matrices, parameter estimations in white or colour noise environments are discussed.

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The optimal encodings for biased associa
The optimal encodings for biased association in linear associative memories
โœ Yee Leung; Tian-Xin Dong; Zong-Ben Xu ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 141 KB

In this paper, optimal encoding schemes for linear associative memories are derived for biased association under both the white-noise and colored-noise situations. Analysis and simulation results all show that the biased encodings thus derived are optimal and superior to existing models in their per