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The optimum networks obtained through the training are used to predict the transformed parameters of the target speaker from the source speaker.
The Dual Entropy-Multiobjective Optimization (DEMO) model which has been demonstrated to be sufficiently robust for designing optimum networks in a large basin was used in these analyses.
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· AriesoACP – Automated network planning technology that searches all possible configurations to deliver optimum network designs.
In case 5, optimal reconfiguration and capacitor placement simultaneously leads to the most optimum network condition.
So, NME entity determines the optimum network selection, based on certain selection rules.
Using IB may give us the optimum network performance as it reduces the collision to minimum.
Therefore, designing proper channel paths is an effective factor in the optimum network design.
The degree of approximation was employed as selective criterion of the optimum network parameters.
An optimum network architecture has been established which can effectively learn the pattern.
The degree of approximation was employed as the selective criterion of the optimum network parameters.
To determine the optimum network, RMSE was calculated for different combination of networks as shown in Table 3.
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