Exponential Stability of Recurrent Neural Networks with Impulsive and Stochastic Effects

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 5 in Stability Analysis of Neural Networks, 2021, pp 139-179 from Springer

Abstract: 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.

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_5

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

DOI: 10.1007/978-981-16-6534-9_5

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 ().