Journal
ADVANCES IN DIFFERENCE EQUATIONS
Volume 2019, Issue 1, Pages -Publisher
SPRINGER
DOI: 10.1186/s13662-019-2298-7
Keywords
Time-varying neural networks; Resilient state estimation; Randomly varying nonlinearities; Missing measurements; H-infinity performance; Variance constraint
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Funding
- Outstanding Youth Science Foundation of Heilongjiang Province of China [JC2018001]
- National Natural Science Foundation of China [61673141]
- Fok Ying Tung Education Foundation of China [151004]
- Natural Science Foundation of Heilongjiang Province of China [A2018007]
- Fundamental Research Funds in Heilongjiang Provincial Universities of China [135209250]
- Educational Research Project of Qiqihar University of China [2017028]
- Alexander von Humboldt Foundation of Germany
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This paper addresses the resilient H-infinity state estimation problem under variance constraint for discrete uncertain time-varying recurrent neural networks with randomly varying nonlinearities and missing measurements. The phenomena of missing measurements and randomly varying nonlinearities are described by introducing some Bernoulli distributed random variables, in which the occurrence probabilities are known a priori. Besides, the multiplicative noise is employed to characterize the estimator gain perturbation. Our main purpose is to design a time-varying state estimator such that, for all missing measurements, randomly varying nonlinearities and estimator gain perturbation, both the estimation error variance constraint and the prescribed H-infinity performance requirement are met simultaneously by providing some sufficient criteria. Finally, the feasibility of the proposed variance-constrained resilient H-infinity state estimation method is verified by some simulations.
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