Robust dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties

Title
Robust dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties
Author(s)
박주현오쟁광[오쟁광]H. Su[H. Su]J. Chu[J. Chu]
Keywords
EXPONENTIAL STABILITY ANALYSIS; H-INFINITY CONTROL; DISTRIBUTED DELAYS; ASYMPTOTIC STABILITY; GLOBAL DISSIPATIVITY; SYSTEMS; DISCRETE; STABILIZATION; PARAMETERS; CRITERION
Issue Date
201208
Publisher
SPRINGER
Citation
NONLINEAR DYNAMICS, v.69, no.3, pp.1323 - 1332
Abstract
This paper investigates the problem of robust dissipativity analysis for uncertain neural networks with time-varying delay. The norm-bounded uncertainties enter into the neural networks in randomly ways, and such randomly occurring uncertainties (ROUs) obey certain mutually uncorrelated Bernoulli distributed white noise sequences. By employing the linear matrix inequality (LMI) approach, a sufficient condition is established to ensure the robust stochastic stability and dissipativity of the considered neural networks. Some special cases are also considered. Two numerical examples are given to demonstrate the validness and the less conservatism of the obtained results.
URI
http://hdl.handle.net/YU.REPOSITORY/27490http://dx.doi.org/10.1007/s11071-012-0350-1
ISSN
0924-090X
Appears in Collections:
공과대학 > 전기공학과 > Articles
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