Novel Cascade Spline Architectures for the Identification of Nonlinear Systems
In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless no...
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| Autor*in: |
Scarpiniti, Michele [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
nonlinear cascade adaptive architectures nonlinear cascade adaptive filters |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on circuits and systems / 1 - New York, NY : Institute of Electrical and Electronics Engineers, 1992, 62(2015), 7, Seite 1825-1835 |
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| Übergeordnetes Werk: |
volume:62 ; year:2015 ; number:7 ; pages:1825-1835 |
| Links: |
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| DOI / URN: |
10.1109/TCSI.2015.2423791 |
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| Katalog-ID: |
OLC195925135X |
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| 520 | |a In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. | ||
| 650 | 4 | |a Adaptation models | |
| 650 | 4 | |a nonlinear filters | |
| 650 | 4 | |a nonlinear functions | |
| 650 | 4 | |a nonlinear adaptive filters | |
| 650 | 4 | |a gradient-based techniques | |
| 650 | 4 | |a structural blocks | |
| 650 | 4 | |a memoryless nonlinear function | |
| 650 | 4 | |a Convergence | |
| 650 | 4 | |a Nonlinear systems | |
| 650 | 4 | |a nonlinear cascade adaptive architectures | |
| 650 | 4 | |a cost function | |
| 650 | 4 | |a sandwich models | |
| 650 | 4 | |a adaptive filters | |
| 650 | 4 | |a Hammerstein-Wiener models | |
| 650 | 4 | |a nonlinear cascade adaptive filters | |
| 650 | 4 | |a cascade spline architectures | |
| 650 | 4 | |a nonlinear system identification | |
| 650 | 4 | |a splines (mathematics) | |
| 650 | 4 | |a online adaptation algorithms | |
| 650 | 4 | |a Indexes | |
| 650 | 4 | |a spline adaptive filters | |
| 650 | 4 | |a learning rates | |
| 650 | 4 | |a identification | |
| 650 | 4 | |a linear filter | |
| 650 | 4 | |a Table lookup | |
| 650 | 4 | |a Digital filters | |
| 650 | 4 | |a Nonlinear functional analysis | |
| 650 | 4 | |a Mathematical research | |
| 650 | 4 | |a Research | |
| 650 | 4 | |a Spline theory | |
| 700 | 1 | |a Comminiello, Danilo |4 oth | |
| 700 | 1 | |a Parisi, Raffaele |4 oth | |
| 700 | 1 | |a Uncini, Aurelio |4 oth | |
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10.1109/TCSI.2015.2423791 doi PQ20160617 (DE-627)OLC195925135X (DE-599)GBVOLC195925135X (PRQ)c1785-8d17477ea8b28c16f8e24aadfb415cc889de9e9cecf36eabbca60641456cae870 (KEY)0213966920150000062000701825novelcascadesplinearchitecturesfortheidentificatio DE-627 ger DE-627 rakwb eng 000 620 DNB Scarpiniti, Michele verfasserin aut Novel Cascade Spline Architectures for the Identification of Nonlinear Systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. Adaptation models nonlinear filters nonlinear functions nonlinear adaptive filters gradient-based techniques structural blocks memoryless nonlinear function Convergence Nonlinear systems nonlinear cascade adaptive architectures cost function sandwich models adaptive filters Hammerstein-Wiener models nonlinear cascade adaptive filters cascade spline architectures nonlinear system identification splines (mathematics) online adaptation algorithms Indexes spline adaptive filters learning rates identification linear filter Table lookup Digital filters Nonlinear functional analysis Mathematical research Research Spline theory Comminiello, Danilo oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in IEEE transactions on circuits and systems / 1 New York, NY : Institute of Electrical and Electronics Engineers, 1992 62(2015), 7, Seite 1825-1835 (DE-627)131043080 (DE-600)1100194-X (DE-576)02804679X 1549-8328 nnns volume:62 year:2015 number:7 pages:1825-1835 http://dx.doi.org/10.1109/TCSI.2015.2423791 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7124548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2059 AR 62 2015 7 1825-1835 |
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10.1109/TCSI.2015.2423791 doi PQ20160617 (DE-627)OLC195925135X (DE-599)GBVOLC195925135X (PRQ)c1785-8d17477ea8b28c16f8e24aadfb415cc889de9e9cecf36eabbca60641456cae870 (KEY)0213966920150000062000701825novelcascadesplinearchitecturesfortheidentificatio DE-627 ger DE-627 rakwb eng 000 620 DNB Scarpiniti, Michele verfasserin aut Novel Cascade Spline Architectures for the Identification of Nonlinear Systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. Adaptation models nonlinear filters nonlinear functions nonlinear adaptive filters gradient-based techniques structural blocks memoryless nonlinear function Convergence Nonlinear systems nonlinear cascade adaptive architectures cost function sandwich models adaptive filters Hammerstein-Wiener models nonlinear cascade adaptive filters cascade spline architectures nonlinear system identification splines (mathematics) online adaptation algorithms Indexes spline adaptive filters learning rates identification linear filter Table lookup Digital filters Nonlinear functional analysis Mathematical research Research Spline theory Comminiello, Danilo oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in IEEE transactions on circuits and systems / 1 New York, NY : Institute of Electrical and Electronics Engineers, 1992 62(2015), 7, Seite 1825-1835 (DE-627)131043080 (DE-600)1100194-X (DE-576)02804679X 1549-8328 nnns volume:62 year:2015 number:7 pages:1825-1835 http://dx.doi.org/10.1109/TCSI.2015.2423791 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7124548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2059 AR 62 2015 7 1825-1835 |
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10.1109/TCSI.2015.2423791 doi PQ20160617 (DE-627)OLC195925135X (DE-599)GBVOLC195925135X (PRQ)c1785-8d17477ea8b28c16f8e24aadfb415cc889de9e9cecf36eabbca60641456cae870 (KEY)0213966920150000062000701825novelcascadesplinearchitecturesfortheidentificatio DE-627 ger DE-627 rakwb eng 000 620 DNB Scarpiniti, Michele verfasserin aut Novel Cascade Spline Architectures for the Identification of Nonlinear Systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. Adaptation models nonlinear filters nonlinear functions nonlinear adaptive filters gradient-based techniques structural blocks memoryless nonlinear function Convergence Nonlinear systems nonlinear cascade adaptive architectures cost function sandwich models adaptive filters Hammerstein-Wiener models nonlinear cascade adaptive filters cascade spline architectures nonlinear system identification splines (mathematics) online adaptation algorithms Indexes spline adaptive filters learning rates identification linear filter Table lookup Digital filters Nonlinear functional analysis Mathematical research Research Spline theory Comminiello, Danilo oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in IEEE transactions on circuits and systems / 1 New York, NY : Institute of Electrical and Electronics Engineers, 1992 62(2015), 7, Seite 1825-1835 (DE-627)131043080 (DE-600)1100194-X (DE-576)02804679X 1549-8328 nnns volume:62 year:2015 number:7 pages:1825-1835 http://dx.doi.org/10.1109/TCSI.2015.2423791 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7124548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2059 AR 62 2015 7 1825-1835 |
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10.1109/TCSI.2015.2423791 doi PQ20160617 (DE-627)OLC195925135X (DE-599)GBVOLC195925135X (PRQ)c1785-8d17477ea8b28c16f8e24aadfb415cc889de9e9cecf36eabbca60641456cae870 (KEY)0213966920150000062000701825novelcascadesplinearchitecturesfortheidentificatio DE-627 ger DE-627 rakwb eng 000 620 DNB Scarpiniti, Michele verfasserin aut Novel Cascade Spline Architectures for the Identification of Nonlinear Systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. Adaptation models nonlinear filters nonlinear functions nonlinear adaptive filters gradient-based techniques structural blocks memoryless nonlinear function Convergence Nonlinear systems nonlinear cascade adaptive architectures cost function sandwich models adaptive filters Hammerstein-Wiener models nonlinear cascade adaptive filters cascade spline architectures nonlinear system identification splines (mathematics) online adaptation algorithms Indexes spline adaptive filters learning rates identification linear filter Table lookup Digital filters Nonlinear functional analysis Mathematical research Research Spline theory Comminiello, Danilo oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in IEEE transactions on circuits and systems / 1 New York, NY : Institute of Electrical and Electronics Engineers, 1992 62(2015), 7, Seite 1825-1835 (DE-627)131043080 (DE-600)1100194-X (DE-576)02804679X 1549-8328 nnns volume:62 year:2015 number:7 pages:1825-1835 http://dx.doi.org/10.1109/TCSI.2015.2423791 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7124548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2059 AR 62 2015 7 1825-1835 |
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10.1109/TCSI.2015.2423791 doi PQ20160617 (DE-627)OLC195925135X (DE-599)GBVOLC195925135X (PRQ)c1785-8d17477ea8b28c16f8e24aadfb415cc889de9e9cecf36eabbca60641456cae870 (KEY)0213966920150000062000701825novelcascadesplinearchitecturesfortheidentificatio DE-627 ger DE-627 rakwb eng 000 620 DNB Scarpiniti, Michele verfasserin aut Novel Cascade Spline Architectures for the Identification of Nonlinear Systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. Adaptation models nonlinear filters nonlinear functions nonlinear adaptive filters gradient-based techniques structural blocks memoryless nonlinear function Convergence Nonlinear systems nonlinear cascade adaptive architectures cost function sandwich models adaptive filters Hammerstein-Wiener models nonlinear cascade adaptive filters cascade spline architectures nonlinear system identification splines (mathematics) online adaptation algorithms Indexes spline adaptive filters learning rates identification linear filter Table lookup Digital filters Nonlinear functional analysis Mathematical research Research Spline theory Comminiello, Danilo oth Parisi, Raffaele oth Uncini, Aurelio oth Enthalten in IEEE transactions on circuits and systems / 1 New York, NY : Institute of Electrical and Electronics Engineers, 1992 62(2015), 7, Seite 1825-1835 (DE-627)131043080 (DE-600)1100194-X (DE-576)02804679X 1549-8328 nnns volume:62 year:2015 number:7 pages:1825-1835 http://dx.doi.org/10.1109/TCSI.2015.2423791 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7124548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2059 AR 62 2015 7 1825-1835 |
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In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. |
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In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. |
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In this paper two novel nonlinear cascade adaptive architectures, here called sandwich models, suitable for the identification of general nonlinear systems are presented. The proposed architectures rely on the combination of structural blocks, each one implementing a linear filter or a memoryless nonlinear function. All the nonlinear functions involved in the adaptation process are based on spline functions and can be easily modified during learning using gradient-based techniques. In particular, a simple form of the on-line adaptation algorithms for the two architectures is derived. In addition, we analytically obtain a bound for the selection of the learning rates involved in the learning algorithms, in order to guarantee a convergence towards a minimum of the cost function. Finally, some experimental results demonstrate the effectiveness of the proposed method. |
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