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Stabilization of Delayed Chaotic Neural Networks by Periodically Intermittent Control

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Abstract

This paper studies the exponential stabilization of delayed chaotic neural networks (DCNNs) using what is called periodically intermittent control. An exponential stability criterion for the controlled neural networks, together with its simplified version, is established by using the Lyapunov function and Halanay inequality. The feasible region of control parameters is estimated in a rigorous way. Theoretical results and numerical simulations show that the continuous-time DCNN can be stabilized by intermittent feedback control with nonzero duration.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400030, China

    Junjian Huang, Chuandong Li & Qi Han

Authors
  1. Junjian Huang
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  2. Chuandong Li
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  3. Qi Han
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Corresponding author

Correspondence to Chuandong Li.

Additional information

The work described in this paper was partially supported by NSFC (Grant No. 60574024) and Program for New Century Excellent Talents in University of China (NCET-06-0764).

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Huang, J., Li, C. & Han, Q. Stabilization of Delayed Chaotic Neural Networks by Periodically Intermittent Control. Circuits Syst Signal Process 28, 567–579 (2009). https://doi.org/10.1007/s00034-009-9098-3

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  • DOI: https://doi.org/10.1007/s00034-009-9098-3

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