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On Stability Analysis for Generalized Neural Networks with Time-Varying Delays

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  • M. J. Park
  • O. M. Kwon
  • E. J. Cha

Abstract

This paper deals with the problem of stability analysis for generalized neural networks with time-varying delays. With a suitable Lyapunov-Krasovskii functional (LKF) and Wirtinger-based integral inequality, sufficient conditions for guaranteeing the asymptotic stability of the concerned networks are derived in terms of linear matrix inequalities (LMIs). By applying the proposed methods to two numerical examples which have been utilized in many works for checking the conservatism of stability criteria, it is shown that the obtained results are significantly improved comparing with the previous ones published in other literature.

Suggested Citation

  • M. J. Park & O. M. Kwon & E. J. Cha, 2015. "On Stability Analysis for Generalized Neural Networks with Time-Varying Delays," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, October.
    • Handle: RePEc:hin:jnlmpe:387805
    DOI: 10.1155/2015/387805
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    Cited by:

    1. Shuoting Wang & Kaibo Shi & Jin Yang, 2022. "Improved Stability Criteria for Delayed Neural Networks via a Relaxed Delay-Product-Type Lapunov–Krasovskii Functional," Mathematics, MDPI, vol. 10(15), pages 1-14, August.
    2. Pharunyou Chanthorn & Grienggrai Rajchakit & Sriraman Ramalingam & Chee Peng Lim & Raja Ramachandran, 2020. "Robust Dissipativity Analysis of Hopfield-Type Complex-Valued Neural Networks with Time-Varying Delays and Linear Fractional Uncertainties," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    3. Miaadi, Foued & Li, Xiaodi, 2021. "Impulsive effect on fixed-time control for distributed delay uncertain static neural networks with leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

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