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In practice, PCA results in a transformation matrix, with which the original data matrix is multiplied.
Here normalized fuzzy decision matrix is multiplied with weights of the evaluation attributes.
Step 5. To distinguish the best alternative, the normalized decision matrix is multiplied by the elements of total weight which were obtained in the previous step.
This new matrix is multiplied by the NFR priority vector obtained from the NFR importance matrix to obtain the overall objective (i.e., ranking of variants).
Finally, the inverse matrix is multiplied by the unbalanced value vector, thus solving the correction equation, and the results are saved in the RDDs array (ef).
This matrix is multiplied by the matrix of estimated model parameters, i.e., the result is a sum of estimated population totals of auxiliary variables times the corresponding model parameter estimate, such as ( {widehat{tau}}_{Xj} cdot {widehat{beta}}_j ).
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Last, the membership matrix and the weight factors matrix are multiplied, and the evaluation results can be derived (Agoubi et al. 2016, Feng et al. 2012).
For the complete time interval, the individual transition probability matrices are multiplied accordingly.
These matrices can be multiplied because the first matrix, Matrix A, has 3 columns, while the second matrix, Matrix B, has 3 rows.
The difference is that (21) is multiplied by the matrix x x ∗, next it is integrated over the Euclidean space E m. □.
Matrix (S) expressing the survival probabilities is multiplied element-wise with the population density vector n(t) = [ n M (t), n F (t)]T.
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