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Global Robust Exponential Stability of Stochastic Neutral-Type Neural Networks

In: Stability Analysis of Neural Networks

Author

Listed:
  • Grienggrai Rajchakit

    (Maejo University, Department of Mathematics)

  • Praveen Agarwal

    (Ajman University, Nonlinear Dynamics Research Center)

  • Sriraman Ramalingam

    (Kalasalingam Academy of Research and Education, Department of Mathematics)

Abstract

In this chapter, a novel $$H_{\infty }$$ H ∞ control design to handle the global robust exponential stability analysis with respect to uncertain stochastic neutral-type neural network (USNNN) models with mixed time-varying delays is presented. Both discrete and distributed time delays are considered, which means that the lower and upper bounds can be derived. Firstly, a control law for stabilized and stability of the USNNN models is formulated. Secondly, by employing the LKF principle, Jensen’s integral inequality, new sufficient conditions for the global robust exponential stability of the considered models are established in terms of delay-dependent LMIs. As the conditions obtained are expressed in terms of LMIs, the associated feasibility can be verified easily by using the MATLAB LMI control toolbox. Numerical examples are provided to assess the effectiveness of our proposed theoretical results. A comparison with the existing results in the existing literature indicates the less conservatism of our findings.

Suggested Citation

  • Grienggrai Rajchakit & Praveen Agarwal & Sriraman Ramalingam, 2021. "Global Robust Exponential Stability of Stochastic Neutral-Type Neural Networks," Springer Books, in: Stability Analysis of Neural Networks, chapter 0, pages 217-250, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-6534-9_7
    DOI: 10.1007/978-981-16-6534-9_7
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