Protocol-based variance-constrained distributed secure filtering with measurement censoring
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Systems Science"
DOI: 10.1080/00207721.2022.2080297
Abstract: In this article, the variance-constrained distributed secure filtering issue based on communication protocol is investigated for time-varying stochastic systems with measurement censoring and cyber attacks over wireless sensor networks. In order to accurately characterise the… read more here.
Keywords: constrained distributed; variance constrained; protocol; variance ... See more keywords
Robust Kalman Filter for Linear System With Convex Polytopic Uncertainties
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2022.3170911
Abstract: This brief considers robust Kalman filtering problem for linear discrete-time systems with convex polytopic uncertain parameters. First, we characterize the uncertain parameter matrices as a combination of several vertex matrices. Then, based on the mean… read more here.
Keywords: robust kalman; variance constrained; convex polytopic; filter ... See more keywords
Variance-Constrained Distributed Filtering Under Limited Bit Rates for Time-Varying Systems
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2025.3600831
Abstract: This article concentrates on the variance-constrained distributed filtering problem with the constraint of limited bit rates and imperfect measurements for nonlinear time-varying systems. The measurement outputs undergo the phenomena of sensor saturations and nonlinearities occurring… read more here.
Keywords: variance; distributed filtering; limited bit; variance constrained ... See more keywords
Learning Stochastic Graph Neural Networks With Constrained Variance
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2023.3244101
Abstract: Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, which comes with no guarantee about deviations of particular… read more here.
Keywords: neural networks; variance constrained; stochastic graph; graph neural ... See more keywords
Variance-constrained filtering for nonlinear systems with randomly occurring quantized measurements: recursive scheme and boundedness analysis
Sign Up to like & getrecommendations! Published in 2019 at "Advances in Difference Equations"
DOI: 10.1186/s13662-019-2000-0
Abstract: In this paper, the robust optimal filtering problem is discussed for time-varying networked systems with randomly occurring quantized measurements via the variance-constrained method. The stochastic nonlinearity is considered by statistical form. The randomly occurring quantized… read more here.
Keywords: quantized measurements; boundedness; occurring quantized; variance constrained ... See more keywords