Robust State Estimation for Delayed Neural Networks with Stochastic Parameter Uncertainties

Title
Robust State Estimation for Delayed Neural Networks with Stochastic Parameter Uncertainties
Author(s)
박주현M.J. Park[M.J. Park]O.M. Kwon[O.M. Kwon]S.M. Lee[S.M. Lee]E.J. Cha[E.J. Cha]
Keywords
TIME-VARYING DELAY; MARKOVIAN JUMP PARAMETERS; EXPONENTIAL STABILITY; CONTROL-SYSTEMS; CRITERIA
Issue Date
201503
Publisher
HINDAWI PUBLISHING CORPORATION
Citation
MATHEMATICAL PROBLEMS IN ENGINEERING
Abstract
This paper considers the problem of delay-dependent state estimation for neural networks with time-varying delays and stochastic parameter uncertainties. It is assumed that the parameter uncertainties are affected by the environment which is changed with randomly real situation, and its stochastic information such as mean and variance is utilized in the proposed method. By constructing a newly augmented Lyapunov-Krasovskii functional, a designing method of estimator for neural networks is introduced with the framework of linear matrix inequalities (LMIs) and a neural networks model with stochastic parameter uncertainties which have not been introduced yet. Two numerical examples are given to show the improvements over the existing ones and the effectiveness of the proposed idea.
URI
http://hdl.handle.net/YU.REPOSITORY/33014http://dx.doi.org/10.1155/2015/948391
ISSN
1024-123X
Appears in Collections:
공과대학 > 전기공학과 > Articles
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