Finite horizon state estimation for time-varying neural networks with sensor failure and energy constraint

Zhang, Bin; Rao, Hongxia; Deng, Yunsong; Wu, Hao; Zhu, Yu

doi:10.1016/j.neucom.2019.09.006

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…In practical engineering, sensors often fail owing to various reasons. In addition to the common sensor failure phenomenon (Peng et al, 2018;B. Zhang et al, 2020), sensor gain degradation (He et al, 2008;Y.…”

Section: Introductionmentioning

confidence: 99%

Fault estimation for nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering

Sheng

Gao

2020

Systems Science & Control Engineering

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This paper focuses on the fault estimation problem for a class of nonlinear systems with sensor gain degradation and stochastic protocol (SP) based on strong tracking filtering. The phenomenon of the sensor gain degradation is described by sequences of stochastic variables in a known interval. The stochastic protocol (SP) is used to deal with possible data conflicts in multi-signal transmission. The augmented system is constructed by combining the original system state vectors and the related faults into an augmented state vectors. The strong tracking filter (STF) is designed by introducing a fading factor into the filter structure to solve the problem of burst faults. Finally, a simulation example is given to verify the effectiveness and applicability of the proposed filter.

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Section: Introductionmentioning

confidence: 99%

Fault estimation for nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering

Sheng

Gao

2020

Systems Science & Control Engineering

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State estimation and structure identification of complex networks with time‐varying delays and model uncertainty based on probabilistic graph neural network

Ma,

Wang

2024

Intl J Robust & Nonlinear

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SummaryIn this paper, the state estimation problem and structure identification problem are investigated for an array of complex networks with time‐varying delays and model uncertainty. Model uncertainty is generated by uncertain connections between nodes in complex networks. Combining probabilistic graph model and graph neural network, a probabilistic graph neural network is proposed to deal with the uncertainty, and the multiple linear terms of the errors between nodes are estimated. The purpose of the addressed state estimation problem is to design a state estimator such that, in the presence of the model uncertainty, the estimation error to converge to zero can be guaranteed and the explicit expression of the estimator parameters is given. Based on a Lyapunov function, the parameters of the estimator and the updating laws of the weights of probabilistic graph neural network can be obtained by the solutions to linear matrix inequalities. The purpose of the addressed structure identification problem is to infer a definite graph structure model based on Bayes' theorem in the presence of a stable probabilistic graph neural network and state estimator. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation and structure identification scheme.

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Finite-time decentralized event-triggering non-fragile control for fuzzy neural networks with cyber-attack and energy constraints

Kanakalakshmi

Sakthivel

Karthick

et al. 2021

European Journal of Control

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Finite horizon state estimation for time-varying neural networks with sensor failure and energy constraint

Cited by 5 publications

References 27 publications

Fault estimation for nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering

Fault estimation for nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering

State estimation and structure identification of complex networks with time‐varying delays and model uncertainty based on probabilistic graph neural network

Finite-time decentralized event-triggering non-fragile control for fuzzy neural networks with cyber-attack and energy constraints

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