The functional link adaptive filter (FLAF) represents an effective solution for online nonlinear modeling problems. In this paper, we take into account a FLAF-based architecture, which separates the adaptation of linear and nonlinear elements, and we focus on the nonlinear branch to improve the modeling performance. In particular, we propose a new model that involves an adaptive combination of filters downstream of the nonlinear expansion. Such combination leads to a cooperative behavior of the whole architecture, thus yielding a performance improvement, particularly in the presence of strong nonlinearities. An advanced architecture is also proposed involving the adaptive combination of multiple filters on the nonlinear branch. The proposed models are assessed in different nonlinear modeling problems, in which their effectiveness and capabilities are shown.

Improving nonlinear modeling capabilities of functional link adaptive filters / Comminiello, Danilo; Scardapane, Simone; Scarpiniti, Michele; Parisi, Raffaele; Uncini, Aurelio. - In: NEURAL NETWORKS. - ISSN 0893-6080. - STAMPA. - 69:(2015), pp. 51-59. [10.1016/j.neunet.2015.05.002]

Improving nonlinear modeling capabilities of functional link adaptive filters

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

Abstract

The functional link adaptive filter (FLAF) represents an effective solution for online nonlinear modeling problems. In this paper, we take into account a FLAF-based architecture, which separates the adaptation of linear and nonlinear elements, and we focus on the nonlinear branch to improve the modeling performance. In particular, we propose a new model that involves an adaptive combination of filters downstream of the nonlinear expansion. Such combination leads to a cooperative behavior of the whole architecture, thus yielding a performance improvement, particularly in the presence of strong nonlinearities. An advanced architecture is also proposed involving the adaptive combination of multiple filters on the nonlinear branch. The proposed models are assessed in different nonlinear modeling problems, in which their effectiveness and capabilities are shown.
2015
Functional links; nonlinear modeling; adaptive combination of filters; online learning algorithms
01 Pubblicazione su rivista::01a Articolo in rivista
Improving nonlinear modeling capabilities of functional link adaptive filters / Comminiello, Danilo; Scardapane, Simone; Scarpiniti, Michele; Parisi, Raffaele; Uncini, Aurelio. - In: NEURAL NETWORKS. - ISSN 0893-6080. - STAMPA. - 69:(2015), pp. 51-59. [10.1016/j.neunet.2015.05.002]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/785979
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