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Following these results, the next section shows a comparison between both algorithms in terms of learning and detection time, scalability, use of resources, and scenario dependency.

By choosing α between 0 and 1, the adaptation steps can be continuously varied in between both algorithms, although the relation is not linear.

Significance of the average difference in A z between both algorithms was tested with the Dorfman-Berbaum-Metz method [ 19, 20] treating both readers and cases as random samples.

The differences between algorithms in both high- and low-precision groups are striking.

They can be used in comparison between different algorithms in a standardized way [6].

In addition, it allows a higher degree of hardware reuse between different algorithms in different standards.

Figure 8 Comparison between VPNMN and NLMS algorithms in a noise cancelation application.

We also provide a detailed comparison between these algorithms in terms of complexity and performance.

Practitioners can choose between the algorithms in the second group of methods depending on the requirements of their applications and available computational resources.

The performance differences between the algorithms in the high SNR regime can be well understood when we look at the accuracy of the algorithms measured with the standard deviation of received SNRs.

In general there was good agreement between the algorithms in predicting the direction of change when compared with the experimental data.