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Exponential Stability of Periodic Solutions for Inertial Type BAM Cohen‐Grossberg Neural Networks

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  • Chunfang Miao
  • Yunquan Ke

Abstract

The existence and exponential stability of periodic solutions for inertial type BAM Cohen‐Grossberg neural networks are investigated. First, by properly choosing variable substitution, the system is transformed to first order differential equation. Second, some sufficient conditions that ensure the existence and exponential stability of periodic solutions for the system are obtained by constructing suitable Lyapunov functional and using differential mean value theorem and inequality technique. Finally, two examples are given to illustrate the effectiveness of the results.

Suggested Citation

  • Chunfang Miao & Yunquan Ke, 2014. "Exponential Stability of Periodic Solutions for Inertial Type BAM Cohen‐Grossberg Neural Networks," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
  • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:857341
    DOI: 10.1155/2014/857341
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    References listed on IDEAS

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    1. Chunguang Li & Guangrong Chen & Xiaofeng Liao & Juebang Yu, 2004. "Hopf bifurcation and chaos in a single inertial neuron model with time delay," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 41(3), pages 337-343, October.
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