The basic idea is that when the power of received signal is far from the desired, the control step is increased to reach the desired level faster.
This means that although the asymptotic distribution of the samples well mimics most of the factors of the integrand, the empirical distribution of the finite number of samples can be far from the desired distribution.
However, if the units are not identical, the stability of such a scheme is in question and the point where the scheme converges could be far from the desired optimum.
In addition, when the allocations stray too far from their desired range, you will need to adjust them.
However, in a real restoration problem, we may not possess accurate values of these parameters, making results relatively far from the desired optimum.
However, the output voltage of renewable energy sources tends to be low, and far from the desired dc-link voltage level of grid-connected inverters.
If the compressive sampling process has infinite or even very large variance, the reconstructed signal obtained utilizing traditional approaches is far from the desired original signal.
Instead, we propose to model (fleft (hat {theta }_{tk}, {Huge |}, mathcal {H}_{i}right)) as approximately uniform in regions sufficiently far from the desired source, and with a notch centred at the DOA of the source.
It improves the original model by allowing the contour to be initialized far from the desired particle contour, which is beneficial since the framework relies on automatic contour initialization [15].
The task is error-tolerated, but if the endpoint deviates too far from the desired position, the patient will feel harder to maintain the incorrect effort, so he has to make a proper adjustment in order to accomplish the task.
