Exponential Stability of Discrete-Time Cellular Uncertain BAM 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 8 in Stability Analysis of Neural Networks, 2021, pp 253-274 from Springer

Abstract: Abstract In this chapter, a class of uncertain discrete-time cellular BAMNN models with variable time delays is studied. The global stability in the exponential sense of the considered time-delayed cellular BAMNN model is analyzed by employing a discrete analog type of Halanay-type inequality Halanay-type inequality. We prove the stability conditions by using a time-invariant perturbation matrix, which is often known as parameter uncertainties. Illustrative examples are provided to ascertain the usefulness and flexibility of the proposed method.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-6534-9_8

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DOI: 10.1007/978-981-16-6534-9_8

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