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Delay-dependent stability analysis of neural networks with time-varying delay: A generalized free-weighting-matrix approach

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  • Zhang, Chuan-Ke
  • He, Yong
  • Jiang, Lin
  • Lin, Wen-Juan
  • Wu, Min

Abstract

This paper investigates the delay-dependent stability problem of continuous neural networks with a bounded time-varying delay via Lyapunov–Krasovskii functional (LKF) method. This paper focuses on reducing the conservatism of stability criteria by estimating the derivative of the LKF more accurately. Firstly, based on several zero-value equalities, a generalized free-weighting-matrix (GFWM) approach is developed for estimating the single integral term. It is also theoretically proved that the GFWM approach is less conservative than the existing methods commonly used for the same task. Then, the GFWM approach is applied to investigate the stability of delayed neural networks, and several stability criteria are derived. Finally, three numerical examples are given to verify the advantages of the proposed criteria.

Suggested Citation

  • Zhang, Chuan-Ke & He, Yong & Jiang, Lin & Lin, Wen-Juan & Wu, Min, 2017. "Delay-dependent stability analysis of neural networks with time-varying delay: A generalized free-weighting-matrix approach," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 102-120.
    • Handle: RePEc:eee:apmaco:v:294:y:2017:i:c:p:102-120
    DOI: 10.1016/j.amc.2016.08.043
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    References listed on IDEAS

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