Sampled-data state estimation for Markovian jumping fuzzy cellular neural networks with mode-dependent probabilistic time-varying delays

Title
Sampled-data state estimation for Markovian jumping fuzzy cellular neural networks with mode-dependent probabilistic time-varying delays
Author(s)
박주현R. Rakkiyappan[R. Rakkiyappan]N. Sakthivel[N. Sakthivel]O.M. Kwon[O.M. Kwon]
Keywords
GLOBAL ASYMPTOTIC STABILITY; EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; SYSTEMS; PARAMETERS; DISCRETE; DESIGN; CRITERIA; LEAKAGE; BAM
Issue Date
201309
Publisher
ELSEVIER SCIENCE INC
Citation
APPLIED MATHEMATICS AND COMPUTATION, v.221, pp.741 - 769
Abstract
In this paper, the problem of state estimation for Markovian jumping fuzzy cellular neural networks (FCNNs) using sampled-data with mode-dependent probabilistic time-varying delays is investigated. By developing a delay decomposition approach, the information of the delayed states can be taken into full consideration. By introducing a stochastic variable with a Bernoulli distribution, the information of probability distribution of the time-varying delay is considered and transformed into one with deterministic time-varying delay. The main purpose of this paper is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally asymptotically stable in the mean square. Based on the Lyapunov-Krasovskii functional including triple integral terms and decomposed integral intervals, delay-distribution-dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally two numerical examples are given to illustrate the effectiveness of the proposed theoretical results. (C) 2013 Elsevier Inc. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/28963http://dx.doi.org/10.1016/j.amc.2013.07.007
ISSN
0096-3003
Appears in Collections:
공과대학 > 전기공학과 > Articles
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