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Nonlinear Dynamics

, Volume 91, Issue 4, pp 2503–2522 | Cite as

Delay-dependent dissipativity criteria for Markovian jump neural networks with random delays and incomplete transition probabilities

  • G. Nagamani
  • Young Hoon JooEmail author
  • T. Radhika
Original Paper

Abstract

In this paper, a new double integral inequality which covers the well-known Wirtinger’s double integral inequality has been developed to analyze the dissipativity behavior of continuous-time neural networks involving Markovian jumping parameters with some unknown transition probabilities and random delays. Based on this generalized double integral inequality, the dissipativity conditions are proposed in terms of linear matrix inequalities by constructing an appropriate Lyapunov–Krasovskii functional with some multiple integral terms under the consideration of free-matrix-based integral inequality and Finsler’s lemma approach. Finally, the effectiveness and the advantages of the proposed technique have been exhibited through numerical simulations.

Keywords

Dissipativity Delayed neural networks Markovian jump Partly unknown transition probabilities Probabilistic delays 

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics, Gandhigram Rural InstituteDeemed UniversityGandhigramIndia
  2. 2.School of IT Technology and Control EngineeringKunsan National UniversityKunsanRepublic of Korea
  3. 3.Department of Science and Humanities-MathematicsKarpagam UniversityCoimbatoreIndia

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