Does iterative nonlinear neural adaptive filtering affect the nature of the processed signal?

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 linear/nonlinear nature 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 provide 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 introduced phase space based methods for signal characterisation. A comprehensive analysis on both linear and nonlinear benchmark signals suggests that data-reusing algorithms not only exhibit a performance advantage over the standard algorithms, but also that the processed signal nature matching improves with the order of DR iteration.