Sentence examples for bound for the error from inspiring English sources
The phrase "bound for the error" is not correct and does not convey a clear meaning in written English.
It may be intended to express a destination related to an error, but it lacks clarity and proper context.
Example: "The system seems to be bound for the error, and we need to troubleshoot it immediately."
Alternatives: "headed for the error" or "destined for the error".
Most algorithms provide the acceptable bound for the error between y and Φs [17 26].
In the following theorem, we find a bound for the error (vert mu^-mu_{N,k}vert ).
The first subsection is related to presenting a bound for the error of the proposed method; meanwhile, in the second subsection, the stability of the numerical solution is investigated briefly.
Finally, we can gain the result of delay bound for the error system: d i k ≤ e i k + n − 1 L max R + l i k R + l i k r i.
The optimal bound for the error on the measurement of Earth's Schwarzschild radius is given by: begin{aligned} frac{vertDelta r_{S}vert}{r_{S}} geqfrac{1}{sqrt{2N}sinh(s)} frac{sigma}{Omega} vert delta_{S} vert^{-1}.
This represents the lower bound for the error.
In the former case, we chose to focus our attention on r t o l, as it plays a somewhat similar role to ε in the latter ones, i.e. as a relative bound for the errors.
Under a non line of sight (NLOS) condition, the roll off factor has negligible effect on error bounds, while under LOS condition, a higher roll-off factor helps to improve the bound for the timing error, possibly due to the sharper form of the first arrival in this case, related to the increase in the bandwidth.
A state observer is designed by dominating the incrementally homogeneous nonlinearities of the observation error system with its linear approximation, while gain adaptation and incremental observability guarantee an asymptotic upper bound for the estimation error depending on the limsup of the norm of the measurement noise.
A state observer is designed by dominating the incrementally homogeneous nonlinearities of the observation error system with its linear approximation, while gain adaptation and incremental observability guarantee an asymptotic upper bound for the estimation error depending on the limsup of the norm of the measuremen noise.
The bound for the localization error is (33).
