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Preliminary indications show that the "minimum operator" approach is a more useful aggregation method than additive and multiplicative techniques.
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The following equations are obtained in fuzzy goal programming approach at tactical level with minimum operator: Maximize λ Subject to λ ≤ μ TCS λ ≤ μ TCD λ ≤ μ TCP 0 ≤ λ ≤ 1 Open image in new window (4.12).
The following are the equations in a fuzzy goal programming approach at the strategic level with minimum operator: Maximize λ Subject to λ ≤ μ SCC λ ≤ μ VF Open image in new window (4.8).
While the minimum operator works in actually populating the matrix.
These fuzzy goals are aggregated using minimum operator.
However, the median operator is computationally more complex than the minimum operator.
Both encoding schemes are equivalent, since one can move from one to the other by applying Weyl's displacement operator (hat{D} alpha)) to both states.1 In the minimum-error approach, the theoretical minimum for the probability of error is given by the Helstrom formula for discriminating two pure states [20].
The advantage of combining EEG and MEG data was also evaluated using other inverse operators, such as sparse source reconstruction (Ding and Yuan 2013) on simulated data, linearly constrained minimum variance beamformer approach on simulated and auditory data (Hong et al. 2013) or Multiple Sparse Prior methods on face evoked responses (Henson et al. 2009).
Minimum distance to approach the end terminal.
At minimum a behavioral approach should be attempted.
Correspondingly, it may be argued that the educational approaches presented here (the operator's manual approach, the methodology approach, and the organizational approach) are idiosyncratic.
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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