• Aug 09, 2018 News![CFP] The annual meeting of IJFCC Editorial Board, ICCTD 2019, will be held in Prague, Czech Republic during March 2-4, 2019.   [Click]
  • Aug 09, 2018 News!IJFCC Vol. 6, No. 1-No. 3 has been indexed by EI (Inspec).   [Click]
  • Dec 24, 2018 News!The papers published in Vol.7, No.1-No.2 have all received dois from Crossref.
General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Cherry L. Chan
    • Abstracting/ Indexing: Google Scholar,  Crossref, Electronic Journals LibraryEI (INSPEC, IET), etc.
    • E-mail:  ijfcc@ejournal.net 
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2015 Vol.4(5): 328-335 ISSN: 2010-3751
doi: 10.18178/ijfcc.2015.4.5.411

Dynamic Analysis of Coupled Binary Cow Patch Cellular Neural Networks

Min Li, Lequan Min, and Mian Wang
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).


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.

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