Global Robust Exponential Stability of Stochastic Neutral-Type Neural Networks

Grienggrai Rajchakit (), Praveen Agarwal () and Sriraman Ramalingam ()
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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

Chapter Chapter 7 in Stability Analysis of Neural Networks, 2021, pp 217-250 from Springer

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-6534-9_7

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DOI: 10.1007/978-981-16-6534-9_7

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