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

C. Rajivganthi, F. A. Rihan, S. Lakshmanan

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)127-137
Number of pages11
JournalNeural Computing and Applications
Volume31
Issue number1
DOIs
Publication statusPublished - Jan 18 2019

Keywords

  • Complex-valued BAM neural networks
  • Delays
  • Dissipativity
  • Matrix measure

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Dissipativity analysis of complex-valued BAM neural networks with time delay'. Together they form a unique fingerprint.

Cite this