Design of Multi-Valued Cellular Neural Networks for Associative Memory
Design of Multi-Valued Cellular Neural Networks for Associative Memory
复制标题
联想记忆多值细胞神经网络设计
DOI：
10.1109/sice.2006.315057
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
T. Imamura
中科院分区：
文献类型：
--
作者：
Zhong Zhang;Takuma Akiduki;T. Miyake;T. Imamura
本文讨论了实现联想记忆的细胞神经网络（CNN）的多值输出函数的设计。 CNN 的输出函数是分段线性函数，由饱和和非饱和范围组成。定义了输出函数的新结构，称为“基本波形”。 n级饱和范围是由n-1个基本波形相加生成的。因此，多值模式的联想记忆的创建已经成功，计算机实验结果表明了该方法的有效性。这项研究的结果可以扩大 CNN 作为联想记忆的应用范围
This paper discusses the design of multi-valued output functions of cellular neural networks (CNNs) implementing associative memories. The output function of the CNNs is a piecewise linear function which consists of a saturation and non-saturation range. The new structure of the output function is defined, and is called the "basic waveform". The saturation ranges with n levels are generated by adding n-1 basic waveforms. Consequently, creating an associative memory of multi-valued patterns has been successful, and computer experiment results show the validity of the proposed method. The results of this research can expand the range of applications of CNNs as associative memories