论文标题
非线性自适应过滤器的稀疏正规化合并
Combined Sparse Regularization for Nonlinear Adaptive Filters
论文作者
论文摘要
非线性自适应过滤器通常会显示出一些稀疏的行为，因为并非所有系数对于任何非线性的建模都同样有用。最近，已经提出了针对非线性过滤器的一类比例算法，以利用其系数的稀疏性。但是，根据问题的不同，对成本功能的规范惩罚可能并不总是适当的。在本文中，我们基于一种基于块的方法引入了一种自适应组合方案，该方案涉及两个具有不同正则化的非线性过滤器，允许与单个规则相比，实现始终具有出色的性能。提出的方法在非线性系统识别问题中进行了评估，显示其在利用在线合并正规化方面的有效性。
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.
