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Sufficient conditions under which the proposed design gives decreasing and ultimately bounded estimation error are provided.
Exact formulas for the bias and the variance of the position estimation error are provided for two specific two-dimensional trajectories straight line and periodic.
Accuracy, stability, and convergence of the semidiscrete approximation to Maxwell's equations is established rigorously and bounds on the growth of the global divergence error are provided.
Definition of system, system of systems, systems engineering, human systems integration, human error are provided, and how all these come together.
Sufficient delay-independent conditions for ensuring robust performances (attenuation level) and dynamic performances (exponential convergence) of the estimation error are provided in terms of LMIs using the Lyapunov-Krasovskii method.
In this section, two numerical examples to assess the feasibility and effectiveness of Algorithms 3.1 3.2 in terms of iteration number (denoted by "IT"), computing time (in seconds, denoted by "CPU"), and the relative error (denoted as "Error") are provided.
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Moreover, the formal stability analysis of the estimation error is provided by using the Lyapunov-based approach.
A characterization of the ℓ∞ identification error is provided, showing that the optimal input is the one that minimizes the ℓ∞ radius of a suitable set.
Furthermore, the connective weights of the PIDNN are trained on-line and the convergence analysis of the regulating error is provided using a discrete-type Lyapunov function.
No distance error was provided.
If the selection is wrong, an explanation of the error is provided.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com