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This paper is concerned with the non-fragile H∞ state estimation problem for a class of discrete-time networked system with probabilistic diverging disturbance and multiple missing measurements.
This paper focuses on designing the H∞ state estimator for a class of discrete-time switched neural networks with time-varying delays, multiple missing measurements and sojourn probabilities.
This paper is concerned with the recursive filtering problem for a class of time-varying nonlinear stochastic systems in the presence of event-triggered transmissions and multiple missing measurements with uncertain missing probabilities.
The aim of the addressed filtering problem is to design a filter such that, in the presence of both the stochastic nonlinearities and multiple missing measurements, there exists an upper bound for the filtering error covariance.
The paper [35] presents an application of sensor networks for distributed H∞ consensus filtering with multiple missing measurements.
This paper is concerned with a new distributed H∞-consensus filtering problem over a finite-horizon for sensor networks with multiple missing measurements.
Similar(50)
Completely missing measurements will be handled using multiple imputation.
Only 25 days have missing measurements.
The missing measurements are considered the finite-time framework.
The missing measurements appear in a random way which is governed by missing rates obeying a certain probability distribution.
Three subjects were missing measurements of SPMA.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com