Stability of Markovian Jumping Stochastic Impulsive Uncertain BAM Neural Networks

Grienggrai Rajchakit (), Praveen Agarwal () and Sriraman Ramalingam ()
Additional contact information
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 6 in Stability Analysis of Neural Networks, 2021, pp 181-215 from Springer

Abstract: Abstract This chapter is focused on the global exponential stability analysis of Markovian jumping stochastic impulsive uncertain BAMNN models with leakage, mixed time delays, and $$\alpha $$ α -inverse holder activation functions. By employing the Lyapunov stability theory and the LMIs, we derive a new sufficient condition to ascertain the global exponential stability of the BAMNN models with time-varying delays and leakage delays. The key contribution of this study is that different types of uncertain parameters are introduced into the LKF candidates for studying the exponential stability behaviors of the BAMNN models. Two numerical examples are provided to illustrate the usefulness of the obtained deterministic and uncertain results.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-6534-9_6

Ordering information: This item can be ordered from
http://www.springer.com/9789811665349

DOI: 10.1007/978-981-16-6534-9_6

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().