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Next, the algorithm works in reverse order and recovers a sequence of optimal steps (using the lookup table K p,d) of the stored values of the index k in the recurrence relation (16)) and eventually the optimal path by.
We also found that no evolutionary path takes optimal steps (steps which incur no penalty) in every functional dimension at every network intermediate.
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the optimal step-size, causing fastest convergence.
This optimal step-size depends on the time varying complex gain model.
The optimal step-size is obtained by minimizing the MSD at each iteration.
Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD).
The optimal step size for f RS depends on the satellite signal strength distribution.
Thus, the optimal step size is now {mu}_{mathrm{opt}}=frac{1}{R{left|hright|}^2}.
Finally, a power series that recovers an optimal step length is build in the neighborhood of bifurcation points.
We have shown that an optimal step-size for the complex gain estimation stage can be obtained.
The optimal step size for the FxLMS algorithm lies in the middle of stability interval [43, 44].
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