Variance-constrained resilient $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

Gao, Yong; Hu, Jun; Chen, Dongyan; Du, Junhua

doi:10.1186/s13662-019-2298-7

Cited by 7 publications

(2 citation statements)

References 49 publications

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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%

Tobit Kalman filtering for fractional‐order systems with stochastic nonlinearities under Round‐Robin protocol

Geng

Wang

et al. 2021

Intl J Robust & Nonlinear

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In this article, the Tobit Kalman filtering problem is investigated for a class of discrete time‐varying fractional‐order systems in the presence of measurement censoring and stochastic nonlinearities under the Round‐Robin protocol (RRP). The fractional‐order dynamic model is described by the Grunwald–Letnikov difference equation, and the statistical means are utilized to characterize the stochastic nonlinearities that include state‐dependent stochastic disturbances as a special case. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. Under the RRP scheduling, only one sensor is permitted to transmit its measurement over the network at each time instant. In light of the renowned orthogonality projection principle, a protocol‐based fractional Tobit Kalman filter is devised with the fractional dynamics and stochastic nonlinearities elaborately addressed. In the pursuit of the filter design, a couple of new terms appear which are in relation to the RRP, fractional dynamics and stochastic nonlinearities arise, and these terms are adequately handled recursively or off‐line. Simulation results are provided to demonstrate the usefulness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Tobit Kalman filtering for fractional‐order systems with stochastic nonlinearities under Round‐Robin protocol

Geng

Wang

et al. 2021

Intl J Robust & Nonlinear

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“…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%

Distributed resilient filtering for a class of nonlinear dynamical systems subject to hybrid cyber attacks

Liu

Ding

2021

Asian Journal of Control

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In this paper, the distributed resilient filtering issue is studied for a class of nonlinear dynamical systems subject to hybrid cyber attacks and gain disturbances.The gain perturbation could come from data rounding errors due to the fixed word length and is modeled by multiplicative noises. The addressed hybrid cyber attacks consist of both denial-of-service (DoS) attacks and deception attacks, in which the injected signal obeys a Gaussian mixture distribution. With the help of Kalman filtering theory, an optimally distributed filtering scheme is developed and the desired filter gain is derived by minimizing the filtering error covariance at each time instant. Furthermore, when the statistical information on DoS attacks is not available, the states of DoS attacks are identified in real-time by resorting to a maximum posterior probability estimation approach combined with the classical Bayesian rule. Based on the developed formulas and the identified state, a suboptimal distributed filtering algorithm is established to realize the state estimation. Finally, a simulation example shows the effectiveness of the proposed distributed resilient filtering algorithm.

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Outlier-resistant variance-constrained $$\mathit{H}_{\infty }$$ state estimation for time-varying recurrent neural networks with randomly occurring deception attacks

Gao

et al. 2023

Neural Comput & Applic

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Variance-constrained resilient $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

Cited by 7 publications

References 49 publications

Tobit Kalman filtering for fractional‐order systems with stochastic nonlinearities under Round‐Robin protocol

Tobit Kalman filtering for fractional‐order systems with stochastic nonlinearities under Round‐Robin protocol

Distributed resilient filtering for a class of nonlinear dynamical systems subject to hybrid cyber attacks

Outlier-resistant variance-constrained $$\mathit{H}_{\infty }$$ state estimation for time-varying recurrent neural networks with randomly occurring deception attacks

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