Sentence examples for much better convergence from inspiring English sources

Compared to the filtered-x least-mean-square algorithm, the proposed algorithm has a much better convergence and stability.

The results show that iGSO has much better convergence performance and is easier to implement in comparison with other existing evolutionary algorithms.

Similarly, our FSR-SADMM has the computation complexity of (mathcal{O}(N_{h}log N_{h})) and shares much better convergence performance.

Compared to the existing algorithms, two versions of the proposed algorithms have much better convergence and stability in impulsive noise environments.

Since convergence in position space could not be obtained with more common numerical techniques, this result helps us to corroborate the conjecture that the use of a localized (square-integrable) basis set within the context of light cone quantization can yield much better convergence.

It is revealed by [32] that sinc methods give a much better rate of convergence and more efficient results than classical polynomial methods in the presence of singularities.

In most problems, it is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to two other elitist MOEAs, namely Pareto-archived evolution strategy (PAES) and strength-Pareto evolutionary algorithm (SPEA), which pay special attention to creating a diverse Pareto-optimal front (Deb et al.,2002).

Much better results in terms of convergence characteristics (number of iterations and time required for convergence) are obtained for PX-EM relative to the basic EM algorithm in the random regression.

We show that proposed algorithm has much reduced computational complexity and better convergence performance, as compared with the time-domain FXLMS algorithm.

Since the CQ algorithm for solving the split feasibility problem (SFP) was proposed [1] in order to get better convergence speed, much attention has been paid to improve the variable stepsize of CQ algorithm.

The method is a built-in option in MLwiN and is used in the estimations reported in this paper, resulting in much better mixing and therefore faster convergence and less need for long iteration chains.