Neural Computing and Applications

, Volume 31, Issue 1, pp 127–137 | Cite as

Dissipativity analysis of complex-valued BAM neural networks with time delay

  • C. RajivganthiEmail author
  • F. A. Rihan
  • S. Lakshmanan
Original Article


This paper is concerned with dissipativity analysis of complex-valued bidirectional associative memory (BAM) neural networks (NNs) with time delay. Some novel sufficient conditions that guarantee the dissipativity of complex-valued BAM neural networks (CVBNNs) are obtained by using the inequality techniques, Halanay inequality, and upper right Dini derivative concepts. The complex-valued nonlinear function is separated into its real and imaginary parts to a set of sufficient conditions for the global dissipativity of CVBNNs by using the matrix measure method. Moreover, the global attractive sets are obtained, which are positive invariant sets. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed theoretical results.


Complex-valued BAM neural networks Delays Dissipativity Matrix measure 



The support of the UAE University to execute this work is highly acknowledged and appreciated.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Mathematical Sciences, College of ScienceUnited Arab Emirates UniversityAl AinUnited Arab Emirates
  2. 2.Institute for Intelligent Systems Research and Innovation (IISRI)Geelong Waurn Ponds Campus, Deakin UniversityGeelongAustralia

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