Original Contribution: Further noise rejection in linear associative memories

Authors:
Sheng-Wei Zhang;A. G. Constantinides;Li-He Zou
Affiliations:
Imperial College, UK;Imperial College, UK;Louisiana Tech University, USA
Venue:
Neural Networks
Year:
1992

Citing 2
Cited 1

2-D Invariant Object Recognition Using Distributed Associative Memory

IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Effect of Noise on the Moore-Penrose Generalized Inverse Associative Memory

IEEE Transactions on Pattern Analysis and Machine Intelligence

Noise Performance of Linear Associative Memories

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

It 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.