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Research Papers

Improved Results on Finite-Time Stability Analysis of Neural Networks With Time-Varying Delays

[+] Author and Article Information
S. Saravanan

Department of Mathematics,
Thiruvalluvar University,
Vellore 632 115, Tamilnadu, India
e-mail: saravanantvu@gmail.com

M. Syed Ali

Department of Mathematics,
Thiruvalluvar University,
Vellore 632 115, Tamilnadu, India
e-mail: syedgru@gmail.com

1Corresponding authors.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received June 30, 2017; final manuscript received March 2, 2018; published online May 2, 2018. Assoc. Editor: Dumitru I. Caruntu.

J. Dyn. Sys., Meas., Control 140(10), 101003 (May 02, 2018) (7 pages) Paper No: DS-17-1330; doi: 10.1115/1.4039667 History: Received June 30, 2017; Revised March 02, 2018

This paper investigates the issue of finite time stability analysis of time-delayed neural networks by introducing a new Lyapunov functional which uses the information on the delay sufficiently and an augmented Lyapunov functional which contains some triple integral terms. Some improved delay-dependent stability criteria are derived using Jensen's inequality, reciprocally convex combination methods. Then, the finite-time stability conditions are solved by the linear matrix inequalities (LMIs). Numerical examples are finally presented to verify the effectiveness of the obtained results.

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Figures

Grahic Jump Location
Fig. 1

State trajectories of neural networks (22)

Grahic Jump Location
Fig. 2

State responses of neural networks

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