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Switched Exponential State Estimation and Robust Stability for Interval Neural Networks with Discrete and Distributed Time Delays

Author

Listed:
  • Hongwen Xu
  • Huaiqin Wu
  • Ning Li

Abstract

The interval exponential state estimation and robust exponential stability for the switched interval neural networks with discrete and distributed time delays are considered. Firstly, by combining the theories of the switched systems and the interval neural networks, the mathematical model of the switched interval neural networks with discrete and distributed time delays and the interval estimation error system are established. Secondly, by applying the augmented Lyapunov‐Krasovskii functional approach and available output measurements, the dynamics of estimation error system is proved to be globally exponentially stable for all admissible time delays. Both the existence conditions and the explicit characterization of desired estimator are derived in terms of linear matrix inequalities (LMIs). Moreover, a delay‐dependent criterion is also developed, which guarantees the robust exponential stability of the switched interval neural networks with discrete and distributed time delays. Finally, two numerical examples are provided to illustrate the validity of the theoretical results.

Suggested Citation

  • Hongwen Xu & Huaiqin Wu & Ning Li, 2012. "Switched Exponential State Estimation and Robust Stability for Interval Neural Networks with Discrete and Distributed Time Delays," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
  • Handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:103542
    DOI: 10.1155/2012/103542
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    References listed on IDEAS

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    1. 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.
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