Associative dynamics of color images in a large-scale chaotic neural network
Associative dynamics of color images in a large-scale chaotic neural network
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DOI：
10.1587/nolta.2.508
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Makito Oku;K. Aihara
中科院分区：
文献类型：
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作者：
Makito Oku;K. Aihara
在本文中，我们报告了一种方法来存储彩色图像在一个大规模的混沌神经网络，并检索它们通过使用混沌动力学。在所提出的方法中，彩色图像被转换为二进制代码，通过反转几位稍微修改，并存储在网络中。数值模拟的结果表明，在一定的参数范围内，可以观察到存储模式之间的混沌过渡及其反转模式。我们还比较了五种不同的编码方案的颜色信息，这改变了混沌动力学的外观。此外，如果连接被限制在每个单元的邻近区域中，则会观察到各种波型。
In this paper, we report a way to store color images in a large-scale chaotic neural network and to retrieve them by using chaotic dynamics. In the proposed method, color images are converted to binary codes, modified slightly by inverting a few bits, and stored in the network. The results of numerical simulations show that chaotic transitions among stored patterns and their reverse patterns can be observed within a certain range of parameters. We also compare five different coding schemes of color information, which change the appearance of chaotic dynamics. In addition, if connections are restricted in a neighborhood of each unit, a variety of wave patterns are observed.