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.

Nonlinear system identification using IIR spline adaptive filters / Scarpiniti, Michele; Comminiello, Danilo; Parisi, Raffaele; Uncini, Aurelio. - In: SIGNAL PROCESSING. - ISSN 0165-1684. - 108:(2015), pp. 30-35. [10.1016/j.sigpro.2014.08.045]

Nonlinear system identification using IIR spline adaptive filters

SCARPINITI, MICHELE;COMMINIELLO, DANILO;PARISI, Raffaele;UNCINI, Aurelio
2015

Abstract

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.
2015
nonlinear adaptive filter; spline adaptive filter; Wiener system identification; spline interpolation; least mean square
01 Pubblicazione su rivista::01a Articolo in rivista
Nonlinear system identification using IIR spline adaptive filters / Scarpiniti, Michele; Comminiello, Danilo; Parisi, Raffaele; Uncini, Aurelio. - In: SIGNAL PROCESSING. - ISSN 0165-1684. - 108:(2015), pp. 30-35. [10.1016/j.sigpro.2014.08.045]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/601595
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