2019
DOI: 10.1186/s13662-019-2298-7
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Variance-constrained resilient $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

Abstract: This paper addresses the resilient H ∞ 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.… Show more

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Cited by 7 publications
(2 citation statements)
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“…The distributed variance‐constrained robust filtering problem has been studied in Reference 23 for a class of time‐varying stochastic systems subject to randomly occurring nonlinearities and missing measurements, where a sufficient condition has been provided to guarantee the boundedness of the filtering error covariance. Recently, the resilient state estimation problem has been resolved in Reference 24 for uncertain time‐varying recurrent neural networks with randomly varying nonlinearities and missing measurements, where the variance constraint and the prescribed H ∞ performance have been ensured.…”
Section: Introductionmentioning
confidence: 99%
“…The distributed variance‐constrained robust filtering problem has been studied in Reference 23 for a class of time‐varying stochastic systems subject to randomly occurring nonlinearities and missing measurements, where a sufficient condition has been provided to guarantee the boundedness of the filtering error covariance. Recently, the resilient state estimation problem has been resolved in Reference 24 for uncertain time‐varying recurrent neural networks with randomly varying nonlinearities and missing measurements, where the variance constraint and the prescribed H ∞ performance have been ensured.…”
Section: Introductionmentioning
confidence: 99%
“…However, such an assumption is in fact unrealistic since the filters may be subject to various inaccuracies such as uncertain parameter of circuit components, data rounding errors due to the fixed word length, and changes of operation conditions, which can reduce the performance of filtering algorithms. Hence, it is meaningful to design a resilient filter to restrain the influence from gain perturbations [27][28][29]. For instance, a robust nonfragile Kalman filter has been designed in Yang and Wang [30] for a class of linear systems with norm-bounded uncertainties.…”
Section: Introductionmentioning
confidence: 99%