Cognitive Neurodynamics

, Volume 10, Issue 4, pp 339–351 | Cite as

Passivity of memristor-based BAM neural networks with different memductance and uncertain delays

  • R. Anbuvithya
  • K. Mathiyalagan
  • R. Sakthivel
  • P. Prakash
Research Article


This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov–Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.


Memristor BAM neural networks Passivity Linear matrix inequality Uncertain delay 



The work of R. Anbuvithya was supported by the Department of Science and Technology, Government of India, New Delhi through the Grant No. DST/INSPIRE Fellowship/2011-IF110718.


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • R. Anbuvithya
    • 1
  • K. Mathiyalagan
    • 2
  • R. Sakthivel
    • 3
    • 4
  • P. Prakash
    • 5
  1. 1.Department of MathematicsNational Institute of TechnologyTiruchirappalliIndia
  2. 2.Department of MathematicsAnna University-Regional CentreCoimbatoreIndia
  3. 3.Department of MathematicsSri Ramakrishna Institute of TechnologyCoimbatoreIndia
  4. 4.Department of MathematicsSungkyunkwan UniversitySuwonThe Republic of Korea
  5. 5.Department of MathematicsPeriyar UniversitySalemIndia

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