Neural Computing and Applications

, Volume 27, Issue 3, pp 549–556

Finite-time stability criteria for a class of fractional-order neural networks with delay

  • Liping Chen
  • Cong Liu
  • Ranchao Wu
  • Yigang He
  • Yi Chai
Original Article

DOI: 10.1007/s00521-015-1876-1

Cite this article as:
Chen, L., Liu, C., Wu, R. et al. Neural Comput & Applic (2016) 27: 549. doi:10.1007/s00521-015-1876-1

Abstract

Finite-time stabilities of a class of fractional-order neural networks delayed systems with order \(\alpha {:}\)\(0<\alpha \le 0.5\) and \(0.5<\alpha <1\) are addressed in this paper, respectively. By using inequality technique, two new delay-dependent sufficient conditions ensuring stability of such fractional-order neural networks over a finite-time interval are obtained. Obtained conditions are less conservative than that given in the earlier references. Two numerical examples are given to show the effectiveness of our proposed method.

Keywords

Finite-time stabilityFractional orderNeural networkDelayed systems

Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  • Liping Chen
    • 1
  • Cong Liu
    • 1
  • Ranchao Wu
    • 2
  • Yigang He
    • 1
  • Yi Chai
    • 3
  1. 1.School of Electrical Engineering and AutomationHefei University of TechnologyHefeiChina
  2. 2.School of MathematicsAnhui UniversityHefeiChina
  3. 3.School of AutomationChongqing UniversityChongqingChina