  Nonlinear adaptive filters often show some sparse behavior due to the fact
that not all the coefficients are equally useful for the modeling of any
nonlinearity. Recently, a class of proportionate algorithms has been proposed
for nonlinear filters to leverage sparsity of their coefficients. However, the
choice of the norm penalty of the cost function may be not always appropriate
depending on the problem. In this paper, we introduce an adaptive combined
scheme based on a block-based approach involving two nonlinear filters with
different regularization that allows to achieve always superior performance
than individual rules. The proposed method is assessed in nonlinear system
identification problems, showing its effectiveness in taking advantage of the
online combined regularization.
