Abstract—Nature abounds with complex patterns emerging
from biological, chemical, physical and social systems. Cellular
Neural Networks (CNNs) may produce patterns similar to those
found in nature, which implies that CNNs may be used as
prototypes to describe some systems in nature. The Cow Patch
CNNs introduced by Chua et al. can generate pattern that cow
patches and checkerboards coexist from any random initial
pattern. In order to investigate the characteristics of the Binary
Cow Patch CNNs, this study introduces concepts of so-called
inherent (final) active, inherent (final) passive, and inherent
(final) neutral for pattern pixels, and proposes Global Task and
Local Rules of the Binary Cow Patch CNNs, and establishes a
set of theorems. Three simulation examples have been carried
out to verify the effectiveness of theoretical results.
Index Terms—Binary cow patch CNN, initial state, binary
image, global task, local rules.
The authors are with the School of Mathematics and Physics, University
of Science and Technology Beijing, Beijing 100083, PR China (e-mail:
limin_mu@126.com, minlequan@sina.com, wangmian14@163.com).
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Cite: Min Li, Lequan Min, and Mian Wang, "Dynamic Analysis of Coupled Binary Cow Patch Cellular Neural Networks," International Journal of Future Computer and Communication vol. 4, no. 5, pp. 328-335, 2015.
