Sentence examples for gain convergence from inspiring English sources

Simulation results verify that the ACLAF algorithm outperforms complex-valued LST based adaptive filter (CLAF), complex-valued least mean square (CLMS), complex-valued normalized least mean square (CNLMS), augmented CLMS (ACLMS) and augmented CNLMS (ACNLMS) algorithms for adaptive prediction of noncircular signals in terms of prediction gain, convergence rate and mean square error (MSE).

Contrary to the underlying linear design, the continuously varying trade-off between high-gain convergence rates and low-gain noise transmission demonstrates a significant improvement of the nonlinear design in achieving performance.

Filtering may thus operate, in tandem with amplifying mechanisms (e.g. synaptic transfer gain, network convergence), to counteract this loss of information.

Thus, for values of ν t close to −1, the accelerated algorithm retains the stability and monotonicity of standard EM algorithms while sacrificing a gain in convergence rate.

The accurate online state estimation for some types of nonlinear singularly perturbed systems is challenging due to extensive computational requirements, ill-conditioned gains and/or convergence issues.

Several orders of magnitude gains in convergence and parallel scaling for the canonical problems, and a factor of 5 reduction for the multiphysics problem are achieved using the MG LBM.

The gain in accuracy, convergence and numerical diffusion of this formalism is shown and discussed through its application to various challenging test cases.

Online plant identification is extended by a time-varying convergence gain to improve the performance in the presence of slight error in the frequency of the reference signal.

This procedure, combined with a consistent approximation of the geometric quantities, is done to satisfy the discretized mass conservation equation to machine accuracy, as well as to gain the favorable convergence properties of the Poisson solver.

Second, we compensate for creep in the piezo system to determine a hysteresis model, and then the parameters of the model are used to find an appropriate value of the iteration gain such that convergence of the control algorithm is guaranteed.

We apply the initial contours to each component of combined color space and then merge the results of each component to gain the final convergence result.