Design and Analysis of High-Capacity Associative Memories Based on a Class of Discrete-Time Recurrent Neural Networks
Design and Analysis of High-Capacity Associative Memories Based on a Class of Discrete-Time Recurrent Neural Networks
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DOI：
10.1109/tsmcb.2008.927717
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发表时间：
2008-12
期刊：
影响因子：
--
通讯作者：
Z. Zeng;Jun Wang
中科院分区：
文献类型：
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作者：
Z. Zeng;Jun Wang
提出了一种基于离散递归神经网络的联想记忆综合器的设计方法。所提出的方法使异和自联想记忆合成高存储容量和保证全局渐近稳定。通过外部输入而不是初始条件，由馈送探针检索存储的模式。作为典型的代表，离散时间细胞神经网络（CNN）设计的空间不变的克隆模板进行了详细的检查。特别地，示出了本文中的过程可以基于仅涉及几个设计参数的空间不变克隆模板来确定任何CNN的输入矩阵。文中给出了两个具体的例子和大量的实验结果，以证明所设计的联想存储器的特性和性能。
This paper presents a design method for synthesizing associative memories based on discrete-time recurrent neural networks. The proposed procedure enables both hetero- and autoassociative memories to be synthesized with high storage capacity and assured global asymptotic stability. The stored patterns are retrieved by feeding probes via external inputs rather than initial conditions. As typical representatives, discrete-time cellular neural networks (CNNs) designed with space-invariant cloning templates are examined in detail. In particular, it is shown that procedure herein can determine the input matrix of any CNN based on a space-invariant cloning template which involves only a few design parameters. Two specific examples and many experimental results are included to demonstrate the characteristics and performance of the designed associative memories.