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Exponential Periodicity of Continuous-time and Discrete-Time Neural Networks with Delays

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Abstract

Exponential periodicity of continuous-time neural networks with delays is investigated. Without assuming the boundedness and differentiability of the activation functions, some new sufficient conditions ensuring existence and uniqueness of periodic solution for a general class of neural systems are obtained. Discrete-time analogue of the continuous-time system with periodic input is formulated and we study their dynamical characteristics. The exponential periodicity of the continuous-time system is preserved by the discrete-time analogue without any restriction imposed on the uniform discretization step-size.

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Authors and Affiliations

  1. College of Electric Engineering, Hohai University, Nanjing, 210098, P. R. China

    Changyin Sun

  2. Research Institute of Automation, Southeast University, Nanjing, 210096, P. R. China

    Changyin Sun & Chun-Bo Feng

Authors
  1. Changyin Sun
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  2. Chun-Bo Feng
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Sun, C., Feng, CB. Exponential Periodicity of Continuous-time and Discrete-Time Neural Networks with Delays. Neural Processing Letters 19, 131–146 (2004). https://doi.org/10.1023/B:NEPL.0000023421.60208.30

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  • DOI: https://doi.org/10.1023/B:NEPL.0000023421.60208.30

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