• DocumentCode
    916955
  • Title

    On the Almost Periodic Solution of Cellular Neural Networks With Distributed Delays

  • Author

    Liu, Yiguang ; You, Zhisheng ; Cao, Liping

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Sichuan Univ., Chengdu
  • Volume
    18
  • Issue
    1
  • fYear
    2007
  • Firstpage
    295
  • Lastpage
    300
  • Abstract
    By exponential dichotomy about differential equations, a formal almost periodic solution (APS) of a class of cellular neural networks (CNNs) with distributed delays is obtained. Then, within different normed spaces, several sufficient conditions guaranteeing the existence and uniqueness of an APS are proposed using two fixed-point theorems. Based on the continuity property and some inequality techniques, two theorems insuring the global stability of the unique APS are given. Comparing with known literatures, all conclusions are drawn with slacker restrictions, e.g., do not require the integral of the kernel function determining the distributed delays from zero to positive infinity to be one, and the activation functions to be bounded, etc.; besides, all criteria are obtained by different ways. Finally, two illustrative examples show the validity and that all criteria are easy to check and apply
  • Keywords
    cellular neural nets; delays; differential equations; stability; almost periodic solution; cellular neural networks; differential equations; distributed delays; fixed-point theorems; global stability; Cellular neural networks; Delay effects; Differential equations; H infinity control; Image processing; Integral equations; Kernel; Nonlinear circuits; Stability; Sufficient conditions; Almost periodic solution (APS); cellular neural networks (CNNs); distributed delays; fixed-point theorem; Action Potentials; Algorithms; Biological Clocks; Biomimetics; Cell Physiology; Computer Simulation; Models, Neurological; Nerve Net; Neural Networks (Computer); Nonlinear Dynamics; Periodicity; Synaptic Transmission; Time Factors;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2006.885441
  • Filename
    4049827