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The FDI scheme employs local optimal observers designed according to minimum state estimation variance.
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At the beginning, most of Kalman filters are linear filters, which are the minimum variance state estimation of linear dynamic systems [5].
This paper addresses the optimal unbiased minimum-variance state estimation of descriptor systems in the framework of unknown input filtering.
In this paper, a globally optimal filtering framework is developed for unbiased minimum-variance state estimation for systems with unknown inputs that affect both the system state and the output.
This paper addresses globally optimal unbiased minimum-variance state estimation for systems with unknown inputs that affect both the system and the output with the descriptor Kalman filtering method.
Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough.
And if the first value of iteration deviates far away from its truevalue, the results of iteration easily fall into local minimum and then cause largeerror of initial state estimation.
In this paper, the state estimation problem is addressed through a decentralized optimization scheme with minimum information exchange among subsystems.
In addition, a problem of binary integer linear programming is solved to obtain and optimally place the minimum number of μPMUs necessary to provide a unique solution for the proposed state estimation formulation.
5) Hybrid state estimation (HSE).
a Iterative channel decoding and state estimation, b Iterative channel decoding and state estimation with previous observation, c Iterative channel decoding and state estimation without previous observation.
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