Applying the adaptive transmission using WPFS, the weighting factors p u for each adaptive user u = 1, …., U A represented by the weighting vector p = [ p 1, …., p U A ] have to be determined such that the user demands D are fulfilled.
To balance the analysis of variance equation, both the left-hand side (EBVs) and the right-hand side (genotypes) were multiplied by the weighting vector, similarly to Morsci et al. (2006).
The weighting vector A can be solved as: A = W − 1 ⋅ Q (4) To calibrate live measured data points P ¯ i (i = 1 t o M ), a new system matrix W ¯ is calculated and multiplied by the weighting vector A, Q ¯ = W ¯ ⋅ A (5 where W ¯ i, j = ((P ¯ i − P j ) 2 + ε 2 ) 1 / 2, i = 1 t o M, j = 1 t o N (6 and Q ¯ is calibrated positions for P ¯.
end{aligned}The (mathrm{OPV}_i ) of each alternative can be obtained by Eq. (13) and the weighting vector provided by DM (see Table 5), the results of (mathrm{OPV}_i ) and the ranking of alternatives based on (mathrm{OPV}_i ) are shown in Table 8.
Subjective assessments are to be made by DM to distinguish the weighting vector and the decision matrix Y ˜ ij ; i = 1, …, m ; j = 1, …, n, using the linguistic terms (each Y ˜ ij is demonstrated by Y ˜ ij = y ij l, y ij m, y ij r ).
When (theta ) is large, the optimal solution is obtained on the search line specified by the weight vector, as shown in Fig. 10b, c.
When (theta ) is small, the best solution is not on the search line specified by the weight vector, as shown in Fig. 10a.
The root model describes the overall characteristics of the target, and the part model describes the local detail features of the target; the relationship between the parts and between the part and the whole is not fixed but is adaptively adjusted by the weight vector so that the model can be deformed to adapt to accurate detection of different shape and posture targets.
Furthermore, the total consumed power to form a virtual MIMO link becomes a function of the transmitted power in the reverse link P t, and how this is distributed between the mobile and the relay stations, which is defined by the weight vector, w, when implementing spatial diversity and by the water filling coefficients ζ i, i=1,2,…,J, for the spatial multiplexing case.
The wrapper method we describe in this paper focuses on estimating a relevance distribution encoded by the weight vector; such a distribution becomes instrumental in the selection of significant variables.
For classifications of (clear vs. rot) and (NV vs. rotNV), the weight vector values were multiplied by the averaged response to the intelligible trials in each instance (i.e. the average of the clear trials was multiplied by the weight vector from the classification of (clear vs. rot) and the average of the NV trials was multiplied by the weight vector from the classification of (NV vs. rotNV).
