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

被引：0

作者：

Magori, Y ^{[1
]}

Kamio, T ^{[1
]}

Fujisaka, H ^{[1
]}

Morisue, M ^{[1
]}

机构：

[1] Hiroshima City Univ, Dept Informat Machines & Interfaces, Asaminami Ku, Hiroshima 7313194, Japan

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

At present, we have proposed associative memories using multilayer perceptrons, (MLP's) 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.

引用

页码：2534 / 2539

页数：6

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