absolute error

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absolute error

[′ab·sə‚lüt ′er·ər]
(mathematics)
In an approximate number, the numerical difference between the number and a number considered exact.
(ordnance)
Shortest distance between the center of impact or the center of burst of a group of shots and the point of impact or burst of a single shot within the group.
Error of a sight consisting of its error in relation to a master service sight with which it is tested and of the known error of the master service sight.
References in periodicals archive ?
We can not quantize the coordinates of approximation nodes (post-quantization approach), because in such a case we cannot guarantee that a final approximation error is below the given error bound [[epsilon].sub.j] (Kolesnikov and Akimov, 2007).
For a large class of planar structures, the approximation error for [A.sub.d] tends to 0 as fast as [([[DELTA].sub.1][[DELTA].sub.2]).sup.3/4].
The Euler Equation error is also important because we know that under certain conditions, the approximation error of the policy function is of the same order of magnitude as the size of the Euler equation residual and correspondingly the change in welfare is of the square order of the Euler equation residual (Santos, 2000).
with the Fourier approximation error bound based on standard results (e.g., see Atkinson [1989, p.
The difference between this direct estimate and the second-order approximation reveals the magnitude of the approximation error. The methods used and our results are reported in the next section of the paper.
Because of the increase in the total number of degrees of freedom, the h-method ultimately reduces the approximation error to zero.
They are also consequences of Weyl's theorem and yield upper bounds for the approximation error of similar order as those stated above.
The importance of this observation is that the proposed scheme gives us an intuition on which approximation of all the approximations results in large approximation error. The approach presented in [14] does not show any intuition on which approximation results in large approximation error, since in [14] the authors only explicitly consider Taylor series expansion for general problem.
The approximation error of the Nystrom method is O([m.sup.-1/2]) [11].
Though the larger the number of neural network nodes, the smaller the approximation error, neural network with a large hidden node number tends to complicate the control and the computation of the control system.
where [W.sup.*] is the ideal weight vector of W and [eta](Z) is any small approximation error which satisfies [absolute value of ([eta](Z))] [less than or equal to] [[eta].sup.*].