Stochastic sampled-data control for state estimation of time-varying delayed neural networks

Title
Stochastic sampled-data control for state estimation of time-varying delayed neural networks
Author(s)
박주현이태희O.M. Kwon[O.M. Kwon]S.M. Lee[S.M. Lee]
Keywords
CONTROL-SYSTEMS; STABILITY-CRITERIA; PASSIVITY ANALYSIS; NEUTRAL TYPE; STABILIZATION; SYNCHRONIZATION; INEQUALITY
Issue Date
201310
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
NEURAL NETWORKS, v.46, pp.99 - 108
Abstract
This study examines the state estimation problem for neural networks with a time-varying delay. Unlike other studies, the sampled-data with stochastic sampling is used to design the state estimator using a novel approach that divides the bounding of the activation function into two subintervals. To fully use the sawtooth structure characteristics of the sampling input delay, a discontinuous Lyapunov functional is proposed based on the extended Wirtinger inequality. The desired estimator gain can be characterized in terms of the solution to linear matrix inequalities (LMIs). Finally, the proposed method is applied to two numerical examples to show the effectiveness of our result. (C) 2013 Elsevier Ltd. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/28788http://dx.doi.org/10.1016/j.neunet.2013.05.001
ISSN
0893-6080
Appears in Collections:
공과대학 > 전기공학과 > Articles
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