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Robust Stability Analysis of Neutral‐Type Hybrid Bidirectional Associative Memory Neural Networks with Time‐Varying Delays

Author

Listed:
  • Wei Feng
  • Simon X. Yang
  • Haixia Wu

Abstract

The global asymptotic robust stability of equilibrium is considered for neutral‐type hybrid bidirectional associative memory neural networks with time‐varying delays and parameters uncertainties. The results we obtained in this paper are delay‐derivative‐dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

Suggested Citation

  • Wei Feng & Simon X. Yang & Haixia Wu, 2014. "Robust Stability Analysis of Neutral‐Type Hybrid Bidirectional Associative Memory Neural Networks with Time‐Varying Delays," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:560861
    DOI: 10.1155/2014/560861
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    References listed on IDEAS

    as
    1. Singh, Vimal, 2007. "Global robust stability of delayed neural networks: Estimating upper limit of norm of delayed connection weight matrix," Chaos, Solitons & Fractals, Elsevier, vol. 32(1), pages 259-263.
    2. Senan, Sibel & Arik, Sabri, 2009. "New results for global robust stability of bidirectional associative memory neural networks with multiple time delays," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 2106-2114.
    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.
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