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Since the output error is an estimation of the original image with background elimination, the original image should be normalized in order to have a normalized error.
The normalized error of distance estimate from normal node i to anchor j is calculated by ε ij = | d ̂ ij − d ij | / d max for i ∈ Ω N, j ∈ Ω A. Note that d ̂ ij ≠ 0. The normalized localization error of normal node i with distance estimates to at least three anchors is γ i = ∥ p ̂ i − p i ∥ / d max.
From results it can be stated that the normalized error obtained from neurofuzzy logic was lower.
Gross error correction is made considering the phase composed normalized error (CNE).
Gross error identification is made through the Largest Normalized Error Test property.
Cyber-attack correction is made considering cyber-attack type and using the composed normalized error (CNE).
Cyber-attack identification is made through the Largest Normalized Error Test property.
The criteria used to compare the results obtained were the normalized error equation (En).
The normalized error value of NPLI for effective efficiency varies between −0.23 and +0.09 with respect to LNE.
Results from this simulation are compared to the output from a professional rendering program, and are found to agree within 10% normalized error.
For A0 and Ae, the normalized error equation was used to compare the results of the laboratories with CENAM's reference values.
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