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Global Exponential Stability Criteria for Bidirectional Associative Memory Neural Networks with Time‐Varying Delays

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  • J. Thipcha
  • P. Niamsup

Abstract

The global exponential stability for bidirectional associative memory neural networks with time‐varying delays is studied. In our study, the lower and upper bounds of the activation functions are allowed to be either positive, negative, or zero. By constructing new and improved Lyapunov‐Krasovskii functional and introducing free‐weighting matrices, a new and improved delay‐dependent exponential stability for BAM neural networks with time‐varying delays is derived in the form of linear matrix inequality (LMI). Numerical examples are given to demonstrate that the derived condition is less conservative than some existing results given in the literature.

Suggested Citation

  • J. Thipcha & P. Niamsup, 2013. "Global Exponential Stability Criteria for Bidirectional Associative Memory Neural Networks with Time‐Varying Delays," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnlaaa:v:2013:y:2013:i:1:n:576721
    DOI: 10.1155/2013/576721
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    References listed on IDEAS

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    1. Lou, Xu Yang & Cui, Bao Tong, 2006. "Global asymptotic stability of delay BAM neural networks with impulses," Chaos, Solitons & Fractals, Elsevier, vol. 29(4), pages 1023-1031.
    2. Park, Ju H. & Lee, S.M. & Kwon, O.M., 2009. "On exponential stability of bidirectional associative memory neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 39(3), pages 1083-1091.
    3. Sheng, Li & Yang, Huizhong, 2009. "Novel global robust exponential stability criterion for uncertain BAM neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 40(5), pages 2102-2113.
    4. Park, Ju H., 2006. "A novel criterion for global asymptotic stability of BAM neural networks with time delays," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 446-453.
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