The aim of this paper is to extend our previous work on a novel and recent class of nonlinear filters called Spline Adaptive Filters (SAFs), implementing the linear part of the Wiener architecture with an IIR filter instead of an FIR one. The new learning algorithm is derived by an LMS approach and a bound on the choice of the learning rate is also proposed. Some experimental results show the effectiveness of the proposed idea. HighlightsAn IIR nonlinear filtering approach based on Spline Adaptive Filters is proposed.The proposed approach is able to solve the identification of Wiener nonlinear systems.The proposed approach outperforms other ones based on adaptive Volterra filters.An upper bound on the choice of the learning rate is derived.
['Michele Scarpiniti', 'Danilo Comminiello', 'Raffaele Parisi', 'Aurelio Uncini']
Nonlinear system identification using IIR Spline Adaptive Filters