Scheduled System Maintenance:
On April 27th, single article purchases and IEEE account management will be unavailable from 2:00 PM - 4:00 PM ET (18:00 - 20:00 UTC).
We apologize for the inconvenience.
By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
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