Sentence examples for mean square norm from inspiring English sources

Using a modified sliding mode control a Markov synchronizing control law is designed and the convergence of error states to the sliding surface in the mean square norm is analytically proved.

(C_{f}) is a subset of (C_{0}) having the elements closest to (C_{i}) ((i=1,2,ldots,m)) in terms of the weighted mean square norm.

Figure4 shows the evolution of the root mean square (RMS) error norm - averaged over all network nodes and Monte Carlo runs - of the emitter position estimates for the RB ReDif-PF and the CbPFa and CbPFb algorithms superimposed to the benchmark RMS error curve for the optimal DcPF algorithm.

> -wrap-foot> χ 2 chi-square, df degrees of freedom; χ 2 / df chisquare to degrees of freedom ratio; RMSEA root mean square error of approximation; NFI normed fit index; IFI incremental fix index; TLI Tucker-Lewis index; CFI comparative fit index Results of the separate confirmatory factor analysis for the Hong Kong and the Philippine samples indicated a good fit.

The BSS method and the Blind least mean square algorithm using Gray's variable norm as a measure of non-Gaussianity of the sources is briefly described and separation results for both simulated and measured data are presented and discussed.

Comparative Fit Index (CFI), Normed Fit Index (NFI), Non-normed Fit Index (NNFI), Root Mean Square Error of Approximation (RMSEA) and its 90% CI were all calculated.

As recommended various fit indices including relative Chi-square (χ/df), Comparative Fit Index (CFI), Incremental Fit Index (IFI), Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) were used [ 32].

By using the Lyapunov functional method and the linear matrix inequality technology, sufficient conditions for the robust mean square stable (RMSS) of the NCSs with an H∞ norm bound γ are obtained.

It relies on the observation that the customary signal-based mean square criterion of LQG optimization may be re-interpreted as a system norm (in particular, the 2-norm), without direct reference to the signals that are involved.

The model fit indices are: Comparative Fit Index – 0.91, Incremental Fit Index – 0.91, Tucker Lewis Index – 0.90, Root Mean Square Residual = 0.057, Root Mean Square Error of Approximation – 0.052, Normed Chi-Square – 1.833.

Secondly, we give the exponential stability in mean square of the exact solution to SLSDDEs by using the definition of logarithmic norm.