Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays

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

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)

Abstract

In this paper, the problem of finite-time stability for a class of fractional-order Cohen–Grossberg BAM neural networks with time delays is investigated. Using some inequality techniques, differential mean value theorem and contraction mapping principle, sufficient conditions are presented to ensure the finite-time stability of such fractional-order neural models. Finally, a numerical example and simulations are provided to demonstrate the effectiveness of the derived theoretical results.

Original languageEnglish
Pages (from-to)1309-1320
Number of pages12
JournalNeural Computing and Applications
Volume29
Issue number12
DOIs
Publication statusPublished - Jun 1 2018

Keywords

  • Banach contraction principle
  • Cohen–Grossberg BAM neural networks
  • Finite-time stability
  • Fractional-order derivative
  • Time delay

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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