Improving nonlinear modeling capabilities of functional link adaptive filters
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 mode...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Comminiello, Danilo [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: Copyright © 2015 Elsevier Ltd. All rights reserved. |
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| Übergeordnetes Werk: |
Enthalten in: Neural networks - Amsterdam : Elsevier, 1988, 69(2015), Seite 51-59 |
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| Übergeordnetes Werk: |
volume:69 ; year:2015 ; pages:51-59 |
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| DOI / URN: |
10.1016/j.neunet.2015.05.002 |
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| 520 | |a 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. | ||
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| 700 | 1 | |a Uncini, Aurelio |4 oth | |
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10.1016/j.neunet.2015.05.002 doi PQ20160617 (DE-627)OLC1957924225 (DE-599)GBVOLC1957924225 (PRQ)c1286-52ea6563fa7125e03e3990f72890325fab70fa87ba2cc052ce73d248ab2d00140 (KEY)0165039420150000069000000051improvingnonlinearmodelingcapabilitiesoffunctional DE-627 ger DE-627 rakwb eng 004 DNB 31.00 bkl 54.00 bkl Comminiello, Danilo verfasserin aut Improving nonlinear modeling capabilities of functional link adaptive filters 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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. Nutzungsrecht: Copyright © 2015 Elsevier Ltd. All rights reserved. Scarpiniti, Michele oth Scardapane, Simone oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in Neural networks Amsterdam : Elsevier, 1988 69(2015), Seite 51-59 (DE-627)130464716 (DE-600)740542-X (DE-576)018263364 0893-6080 nnns volume:69 year:2015 pages:51-59 http://dx.doi.org/10.1016/j.neunet.2015.05.002 Volltext http://www.ncbi.nlm.nih.gov/pubmed/26057613 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_30 GBV_ILN_59 GBV_ILN_70 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4335 31.00 AVZ 54.00 AVZ AR 69 2015 51-59 |
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10.1016/j.neunet.2015.05.002 doi PQ20160617 (DE-627)OLC1957924225 (DE-599)GBVOLC1957924225 (PRQ)c1286-52ea6563fa7125e03e3990f72890325fab70fa87ba2cc052ce73d248ab2d00140 (KEY)0165039420150000069000000051improvingnonlinearmodelingcapabilitiesoffunctional DE-627 ger DE-627 rakwb eng 004 DNB 31.00 bkl 54.00 bkl Comminiello, Danilo verfasserin aut Improving nonlinear modeling capabilities of functional link adaptive filters 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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. Nutzungsrecht: Copyright © 2015 Elsevier Ltd. All rights reserved. Scarpiniti, Michele oth Scardapane, Simone oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in Neural networks Amsterdam : Elsevier, 1988 69(2015), Seite 51-59 (DE-627)130464716 (DE-600)740542-X (DE-576)018263364 0893-6080 nnns volume:69 year:2015 pages:51-59 http://dx.doi.org/10.1016/j.neunet.2015.05.002 Volltext http://www.ncbi.nlm.nih.gov/pubmed/26057613 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_30 GBV_ILN_59 GBV_ILN_70 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4335 31.00 AVZ 54.00 AVZ AR 69 2015 51-59 |
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10.1016/j.neunet.2015.05.002 doi PQ20160617 (DE-627)OLC1957924225 (DE-599)GBVOLC1957924225 (PRQ)c1286-52ea6563fa7125e03e3990f72890325fab70fa87ba2cc052ce73d248ab2d00140 (KEY)0165039420150000069000000051improvingnonlinearmodelingcapabilitiesoffunctional DE-627 ger DE-627 rakwb eng 004 DNB 31.00 bkl 54.00 bkl Comminiello, Danilo verfasserin aut Improving nonlinear modeling capabilities of functional link adaptive filters 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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. Nutzungsrecht: Copyright © 2015 Elsevier Ltd. All rights reserved. Scarpiniti, Michele oth Scardapane, Simone oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in Neural networks Amsterdam : Elsevier, 1988 69(2015), Seite 51-59 (DE-627)130464716 (DE-600)740542-X (DE-576)018263364 0893-6080 nnns volume:69 year:2015 pages:51-59 http://dx.doi.org/10.1016/j.neunet.2015.05.002 Volltext http://www.ncbi.nlm.nih.gov/pubmed/26057613 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_30 GBV_ILN_59 GBV_ILN_70 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4335 31.00 AVZ 54.00 AVZ AR 69 2015 51-59 |
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10.1016/j.neunet.2015.05.002 doi PQ20160617 (DE-627)OLC1957924225 (DE-599)GBVOLC1957924225 (PRQ)c1286-52ea6563fa7125e03e3990f72890325fab70fa87ba2cc052ce73d248ab2d00140 (KEY)0165039420150000069000000051improvingnonlinearmodelingcapabilitiesoffunctional DE-627 ger DE-627 rakwb eng 004 DNB 31.00 bkl 54.00 bkl Comminiello, Danilo verfasserin aut Improving nonlinear modeling capabilities of functional link adaptive filters 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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. Nutzungsrecht: Copyright © 2015 Elsevier Ltd. All rights reserved. Scarpiniti, Michele oth Scardapane, Simone oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in Neural networks Amsterdam : Elsevier, 1988 69(2015), Seite 51-59 (DE-627)130464716 (DE-600)740542-X (DE-576)018263364 0893-6080 nnns volume:69 year:2015 pages:51-59 http://dx.doi.org/10.1016/j.neunet.2015.05.002 Volltext http://www.ncbi.nlm.nih.gov/pubmed/26057613 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_30 GBV_ILN_59 GBV_ILN_70 GBV_ILN_2030 GBV_ILN_4266 GBV_ILN_4335 31.00 AVZ 54.00 AVZ AR 69 2015 51-59 |
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Improving nonlinear modeling capabilities of functional link adaptive filters |
| 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. |
| abstractGer |
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. |
| abstract_unstemmed |
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. |
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| title_short |
Improving nonlinear modeling capabilities of functional link adaptive filters |
| url |
http://dx.doi.org/10.1016/j.neunet.2015.05.002 http://www.ncbi.nlm.nih.gov/pubmed/26057613 |
| remote_bool |
false |
| author2 |
Scarpiniti, Michele Scardapane, Simone Parisi, Raffaele Uncini, Aurelio |
| author2Str |
Scarpiniti, Michele Scardapane, Simone Parisi, Raffaele Uncini, Aurelio |
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| doi_str |
10.1016/j.neunet.2015.05.002 |
| up_date |
2024-07-04T01:43:29.150Z |
| _version_ |
1803610917856870400 |
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| score |
7.3979883 |