Stability and dissipativity analysis of static neural networks with interval time-varying delay

Title
Stability and dissipativity analysis of static neural networks with interval time-varying delay
Author(s)
박주현H.B. Zeng[H.B. Zeng]C.F. Zhang[C.F. Zhang]W. Wang[W. Wang]
Keywords
GLOBAL ASYMPTOTIC STABILITY; EXPONENTIAL STABILITY; DEPENDENT STABILITY; DISCRETE; SYSTEMS; PARAMETERS; PASSIVITY; CRITERIA
Issue Date
201503
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, v.352, no.3, pp.1284 - 1295
Abstract
This paper focuses on the problems of stability and dissipativity analysis for static neural networks (NN) with interval time-varying delay. A new augmented Lyapunov Krasovskii functional is firstly constructed, in which the information on the activation function is taken fully into account. Then, by employing a Wirtinger-based inequality to estimate the derivative of Lyapunov Krasovskii functional, an improved stability criterion is derived for the considered neural networks. The result is extended to dissipafivity analysis and a sufficient condition is established to assure the neural networks strictly dissipative. Two numerical examples are provided to demonstrate the effectiveness and the advantages of the proposed method. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/33113http://dx.doi.org/10.1016/j.jfranklin.2014.12.023
ISSN
0016-0032
Appears in Collections:
공과대학 > 전기공학과 > Articles
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