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Global Stability Analysis of Neural Networks with Constant Time Delay via Frobenius Norm

Author

Listed:
  • N. Mohamed Thoiyab
  • P. Muruganantham
  • Grienggrai Rajchakit
  • Nallappan Gunasekaran
  • Bundit Unyong
  • Usa Humphries
  • Pramet Kaewmesri
  • Chee Peng Lim

Abstract

This paper deals with the global asymptotic robust stability (GARS) of neural networks (NNs) with constant time delay via Frobenius norm. The Frobenius norm result has been utilized to find a new sufficient condition for the existence, uniqueness, and GARS of equilibrium point of the NNs. Some suitable Lyapunov functional and the slope bounded functions have been employed to find the new sufficient condition for GARS of NNs. Finally, we give some comparative study of numerical examples for explaining the advantageous of the proposed result along with the existing GARS results in terms of network parameters.

Suggested Citation

  • N. Mohamed Thoiyab & P. Muruganantham & Grienggrai Rajchakit & Nallappan Gunasekaran & Bundit Unyong & Usa Humphries & Pramet Kaewmesri & Chee Peng Lim, 2020. "Global Stability Analysis of Neural Networks with Constant Time Delay via Frobenius Norm," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, October.
    • Handle: RePEc:hin:jnlmpe:4321312
    DOI: 10.1155/2020/4321312
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    Cited by:

    1. Thoiyab, N. Mohamed & Muruganantham, P. & Zhu, Quanxin & Gunasekaran, Nallappan, 2021. "Novel results on global stability analysis for multiple time-delayed BAM neural networks under parameter uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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