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Attractors of Discrete Cellular Neural Networks

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Artificial Intelligence and Computational Intelligence (AICI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7004))

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Abstract

The dynamic behavior of discrete cellular neural networks (DCNN) with zero threshold value, which is strict, is mainly studied. For the DCNN with zero threshold value and no self-feedback, if a state is a fixed point, then a lot of unstable points are given, and under some conditions these unstable points can converge to the fixed point. In this paper, the properties of k-attractor of the DCNN are mainly studied, and some conditions are obtained under which the k-attractor is a fixed point, and lots of unstable points are attracted to the fixed point. The results obtained here on k-attractor improve the results in the previous references.

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References

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Author information

Authors and Affiliations

  1. Telecommunication Engineering Institute, Air Force Engineering University, Xi’an, 710077, China

    Run-Nian Ma, Gang Wen & Hong Xiao

  2. Key Laboratory of advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, 116622, China

    Hong Xiao

Authors
  1. Run-Nian Ma
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  2. Gang Wen
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  3. Hong Xiao
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Editor information

Editors and Affiliations

  1. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, VIC, Australia

    Hepu Deng

  2. School of Electronics and Information, Tongji University, 201804, Shanghai, China

    Duoqian Miao

  3. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  4. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Ma, RN., Wen, G., Xiao, H. (2011). Attractors of Discrete Cellular Neural Networks. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_35

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  • DOI: https://doi.org/10.1007/978-3-642-23896-3_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23895-6

  • Online ISBN: 978-3-642-23896-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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