State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach

Title
State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach
Author(s)
박주현S. Lakshmanan[S. Lakshmanan]R. Rakkiyappan[R. Rakkiyappan]정호열
Keywords
TIME-VARYING DELAY; EXPONENTIAL STABILITY; CONTROL-SYSTEMS; NEUTRAL-TYPE; DISTRIBUTED DELAYS; NONLINEAR-SYSTEMS; DESIGN; STABILIZATION; SYNCHRONIZATION; INEQUALITY
Issue Date
201307
Publisher
SPRINGER
Citation
NONLINEAR DYNAMICS, v.73, no.1-2, pp.509 - 520
Abstract
In this paper, the sampled-data state estimation problem is investigated for neural networks with time-varying delays. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled data estimator is constructed. Based on the extended Wirtinger inequality, a discontinuous Lyapunov functional is introduced, which makes full use of the sawtooth structure characteristic of sampling input delay. New delay-dependent criteria are developed to estimate the neuron states through available output measurements such that the estimation error system is asymptotically stable. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulations are given to demonstrate the usefulness and effectiveness of the presented results.
URI
http://hdl.handle.net/YU.REPOSITORY/29446http://dx.doi.org/10.1007/s11071-013-0805-z
ISSN
0924-090X
Appears in Collections:
공과대학 > 전기공학과 > Articles
공과대학 > 모바일정보통신공학과 > Articles
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