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With data from the literature and an approximation of the inverse error function, sensitivity analyses were carried out.
The methodology is based on a novel framework, which identifies inverse error dynamics using a Wavelet Neural Network (WNN) with daughter Mexican hat wavelets activation functions.
The assimilation is based on a general inverse method using an L2 norm-type cost function, weighted by the use of inverse error covariance operators.
The Gaussianized value is then obtained by using the inverse cumulative density function (CDF) of a standard Gaussian distribution (i.e., the probit function) as follows: ϕ l m ← 2 erf − 1 2 r m − 1, (41 where erf−1 denotes the inverse error function.
An inverse error built into their surfaces could completely cancel the aberration of the primary.
Mean prediction accuracies were converted to d-prime using: d - prime = 2 ∗ erfinv (2 ∗ accuracy - 1 ) ; erfinv, inverse error function.
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It can offer adaptive compensation in time for model inverse errors caused by the changes of flight conditions and achieve accurate commands tracking.
where erfc−1 is the inverse complementary error function.
For the solution of the inverse problem, error estimates are given for the mass surface density deduced from modeshape interferograms.
Indeed, r g will be modified in each frame accordingly with the inverse of error E in Equation (12).
The type B nodes transmit their state estimates along with the inverse of error covariance matrix to the central base station which fuses the cluster state estimates to generate the overall global state estimate.
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