A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling. (arXiv:2104.09641v2 [cs.LG] UPDATED)

Nonlinear models are known to provide excellent performance in real-world
applications that often operate in non-ideal conditions. However, such
applications often require online processing to be performed with limited
computational resources. To address this problem, we propose a new class of
efficient nonlinear models for online applications. The proposed algorithms are
based on linear-in-the-parameters (LIP) nonlinear filters using functional link
expansions. In order to make this class of functional link adaptive filters
(FLAFs) efficient, we propose low-complexity expansions and frequency-domain …

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