The data-reusing (DR) approach is commonly used to improve the convergence rate and robustness of standard adaptive filters. However, it is largely unknown whether such an approach affects the linearhionlinear natute of the processed signal. It is therefore natural to ask ourselves a question "does iterative nonlinear neural adaptive filtering affect the nature of the processed signal". To help to answer this, we pmvide a quality assessment of both the standard and data-reusing direct gradient algorithms for linear FIR filters and neural networks applied in adaptive filtering. This is achieved based upon some recently intrcduced phase space based methods for signal characterisation. A comprehensivc analysis on b.oth linear ind nonlinear benchmark signals suggests that data-reusing algorithms not only exhihit a performance advantage over the standard algorithms, but also that the processed signal nature matching improves with the order of DR.iteration.
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