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Noise supplement learning algorithm for associative memories using multilayer perceptrons and sparsely interconnected neural networks

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4 Author(s)
Magori, Y. ; Dept. of Inf. Machines & Interfaces, Hiroshima City Univ., Japan ; Kamio, T. ; Fujisaka, H. ; Morisue, M.

At present, we have proposed associative memories using multilayer perceptrons (MLPs) and sparsely interconnected neural networks (SINNs), named MLP-SINN, to improve SINNs without increasing their interconnections. MLP-SINN is more suitable for hardware implementation than SINN with a large number of interconnections. However, the capabilities of MLP and SINN are not effectively used in the conventional MLP-SINN, because they are synthesized independently. In this paper, we propose the noise supplement learning algorithm to improve MLP-SINN associative memories.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003

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