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Exponential Stability of Recurrent Neural Networks with Impulsive and Stochastic Effects

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, the problem of exponential stability analysis of time-delayed recurrent neural networks (RNNa) with impulsive and stochastic effectsStochastic effects under fractional segmentsFractional segments or intervals in delays is investigated. The time delays in discrete terms are time varying in nature. Different from those in the existing literature, the discrete delay interval is separated into fractional segments, which guarantee the availability of the lower and upper bounds for the feasible solutions with accuracy. By constructing a suitable LKF candidate, and with the aid of stability theoryStability theory and inequality techniques, several novel stability criteria are formulated via LMIs to ensure the exponential stability of the considered RNN models in the mean square sense. Two numerical examples are presented to substantiate the superiority and effectiveness of our theoretical outcomes.

Suggested Citation

  • Grienggrai Rajchakit & Praveen Agarwal & Sriraman Ramalingam, 2021. "Exponential Stability of Recurrent Neural Networks with Impulsive and Stochastic Effects," Springer Books, in: Stability Analysis of Neural Networks, chapter 0, pages 139-179, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-6534-9_5
    DOI: 10.1007/978-981-16-6534-9_5
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