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Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays

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  • Chunmei Wu
  • Junhao Hu
  • Yan Li

Abstract

We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neural networks are guaranteed to also be globally exponentially stable. Finally, a numerical simulation example is given to illustrate the presented criteria.

Suggested Citation

  • Chunmei Wu & Junhao Hu & Yan Li, 2015. "Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-12, October.
    • Handle: RePEc:hin:jnddns:278571
    DOI: 10.1155/2015/278571
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    Cited by:

    1. Li, Hui & Kao, Yonggui & Li, Hong-Li, 2021. "Globally β-Mittag-Leffler stability and β-Mittag-Leffler convergence in Lagrange sense for impulsive fractional-order complex-valued neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).

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