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Consistent with the group mean performance analysis, inspection of Figure 3C reveals near complete overlap in the range of scores within the migraine and control groups.
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Section IV presents the mean square performance analysis of SR-NSAF.
Mean-square performance analysis of the SR-NSAF algorithm in the stationary and nonstationary environments.
The simulation results are almost the same with the analytical results, which means the performance analysis is accurate.
In Appendix 1, the mean-square performance analysis of the SR-NSAF is presented in the nonstationary environment.
Also, the theoretical mean-square performance analysis and the stability bound of the proposed algorithms were studied and confirmed by different experiments.
We present the general framework for mean-square performance analysis of the selective partial update affine projection algorithm (SPU-APA) and the family of SPU normalized least mean-squares (SPU-NLMS) adaptive filter algorithms in nonstationary environment.
Therefore, in the following, the energy conservation approach [27] is applied to the SR-NSAF and the mean-square performance analysis of the proposed algorithms are studied in the stationary and nonstationary environments.
The theoretical mean-square performance analysis of the proposed algorithm in the stationary and nonstationary environments is studied based on the energy conservation relation and the steady-state, the transient, and the stability bounds of the SR-NSAF are predicated by the closed form expressions.
Section "Performance results and analysis" presents the main performance analysis.
A mean-square error (MSE) performance analysis of D-LMS is conducted in the presence of a time-varying parameter vector, which adheres to a first-order autoregressive model.
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