Journal
NEUROCOMPUTING
Volume 139, Issue -, Pages 107-113Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2014.02.055
Keywords
Dissipativity-based state estimation; Markov jump; Discrete-time neural networks; Unreliable communication links
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Funding
- National Natural Science Foundation of China [61304066, 61104007, 61104221]
- Natural Science Foundation of Anhui Province [1308085QF119]
- Key Foundation of Natural Science for Colleges and Universities in Anhui province [KJ2012A049]
- Excellent Youthful Talent Foundation of Colleges and Universities of Anhui Province of China [2013SQRL024ZD]
- Research Foundation for Young Scientists of Anhui University of Technology [QZ201314]
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This paper investigates the problem of dissipativity-based state estimator design for Markov jump discrete-time neural networks, where the communication links between the neural network and estimator are assumed to be imperfect. The phenomenon of missing data is modeled by a stochastic variable following the Bernoulli random distribution. The focus is on the design of a Markov switching estimator such that the resulting closed-loop system is dissipative. Some sufficient conditions for the existence of admissible estimator are obtained in terms of linear matrix inequalities. Finally, a numerical example is employed to demonstrate the effectiveness of our proposed approach. (C) 2014 Elsevier B.V. All rights reserved.
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