The y of the [l.sub.0]-norm SSAF is obtained by repeated trials to minimize the steady-state NMSD. We use the input signals generated by [G.sub.1](z) and [G.sub.2](z) for Figures 2-7 and Figures 8 and 9, respectively.
Figure 2 shows the NMSD learning curves of the NSAF, PNSAF, SSAF, and [l.sub.0]-norm SSAF algorithms in the case of SIR = -30 dB.
In Figure 3, to verify the effect of y on convergence performance, the NMSD curves of the [l.sub.0]-SSAF for different y values are illustrated in the case of SIR = -30 dB.
Figure 4 illustrates the NMSD learning curves of the NSAF, PNSAF, SSAF, and [l.sub.0]-norm SSAF algorithms under SIR = -10 dB.
of uniformity between the admission standards for NMSVH (253) and NMSD
(255) Conversely, NMSD's policy for multi-disabled children places
students in its admission policy, while NMSD allows determinations based
(254.) NMSD ADMISSIONS REGULATIONS (on file with author).