Improving nonlinear modeling capabilities of functional link adaptive filters. (September 2015)

• Record Type: Journal Article
• Authors: Comminiello, Danilo; Scarpiniti, Michele; Scardapane, Simone; Parisi, Raffaele; Uncini, Aurelio
• Is Part Of: Neural networks. Volume 69(2015:Sep.)
• Page Start: 51
• Page End: 59
• Publication Date: 2015-09
• Subjects: Functional links -- Nonlinear modeling -- Adaptive combination of filters -- Online learning algorithms; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.05.002
• Languages: English
• ISSNs: 0893-6080

Abstract

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. Highlights: This paper proposes an improved split functional link adaptive filter (SFLAF). The proposed model is characterized by the adaptive combination of two APA filters. An advanced scheme is also proposed involving the combination of multiple filters. The adaptive combinations are performed for all the projections of the APA filters. The proposed models are assessed in three different nonlinear modeling problems.