Sentence examples for mean square performance from inspiring English sources

Section IV presents the mean square performance analysis of SR-NSAF.

In this work, we study the mean square performance for the convex-combined filter using the filtered input signal.

2) We study the mean and mean square performance for the proposed algorithms in a general case, and provide the transient analysis for a specialized case.

We develop detailed analyses in terms of mean and mean square performance for the proposed algorithms and derive mathematical expressions for the mean square deviation (MSD) and the excess mean square error (EMSE).

Specifically, for the general case, in terms of the mean performance, we show that the mean value of the weight error vector approaches zero as iteration numbers go to infinity, which implies the asymptotical convergence of the proposed algorithms; from the perspective of mean square performance, we derive the mathematical expressions for the steady-state MSD and EMSE values.

In this part, we perform analyses for the proposed LTVFF-DRLS and LCTVFF-DRLS algorithms based on mean square performance and derive expressions for the steady-state MSD and EMSE, which are defined as begin{aligned} {MSD}_{k}^{ss}&={lim}_{itoinfty}Eleft[|widetilde{mathbf{w}}_{k,i}|^{2}right] {EMSE}_{k}^{ss}&={lim}_{itoinfty}Eleft[|mathbf{u}_{k,i}^widetilde{mathbf{w}}_{k,i-1}|^{2}right].

Mean-square performance analysis of the SR-NSAF algorithm in the stationary and nonstationary environments.

To evaluate the control laws, a modified mean-square performance index with an infinite control time was proposed.

In Appendix 1, the mean-square performance analysis of the SR-NSAF is presented in the nonstationary environment.

Bounds for the mean-square performance are established on the basis of upper bounds for the solution of the relevant Riccati equation.

Also, the theoretical mean-square performance analysis and the stability bound of the proposed algorithms were studied and confirmed by different experiments.