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Adaptive reconfiguration is ensured by estimating the unknown parameters in near real-time.
This is achieved by estimating the unknown parameters p of the model from measurement data, cf. [4].
By estimating the unknown damage extent of a structure indirectly via observed measurements, the need to relate both quantities, i.e. a calibration of damage extent and measurements, arises.
In analyzing the impulsive phenomenon of a physical system governed by certain differential and integral equations, by estimating the unknown function in the integral inequality of the discontinuous functions, Some properties of the solution of some impulsive differential equations can be studied.
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The reasons why we do not use the likelihood given by the Girsanov transformation for estimating the unknown parameters (Cialenco et al. [14]) are twofold.
Joint analysis is a standard approach to deal with missing data in the context of semi-supervised learning and can be performed by iteratively estimating the parameters by maximizing the PseudoLikelihood Function (PLF) using logistic regression as a first step and estimating the unknown function by optimizing the objective function of the MRF in the second step, till convergence is met [22].
We show that an optimal global measurement, performed jointly over the signal and the training set, provides higher successful identification rates than any learning strategy based on first estimating the unknown amplitude by means of Gaussian measurements on the training set, followed by an adaptive discrimination procedure on the signal.
We consider a class of systems influenced by perturbations that are nonlinearly parameterized by unknown constant parameters, and develop a method for estimating the unknown parameters.
By estimating the norm of unknown parameter vectors instead of themselves, it can avoid the over-estimation problem.
This filter has been designed to solve this problem by estimating the state and unknown measurement biases simultaneously with derivative-free character, leading to a high-precision algorithm for the Mars entry navigation.
By estimating the bounds of the unknown parameters instead of themselves, it can avoid the over-estimation problem.
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