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Delay-Dependent Global Robust Asymptotic Stability Analysis Of Bam Neural Networks With Time Delay: An Lmi Approach

Author

Listed:
  • XU-YANG LOU

    (Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Rd., Wuxi, Jiangsu 214122, P.R. China)

  • BAO-TONG CUI

    (Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Rd., Wuxi, Jiangsu 214122, P.R. China)

Abstract

The global robust asymptotic stability of bi-directional associative memory (BAM) neural networks with constant or time-varying delays is studied. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problem. Some a criteria for the global robust asymptotic stability, which gives information on the delay-dependent property, are derived. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.

Suggested Citation

  • Xu-Yang Lou & Bao-Tong Cui, 2007. "Delay-Dependent Global Robust Asymptotic Stability Analysis Of Bam Neural Networks With Time Delay: An Lmi Approach," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 57-68.
    • Handle: RePEc:wsi:nmncxx:v:03:y:2007:i:01:n:s1793005707000628
    DOI: 10.1142/S1793005707000628
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