Hysteresis compensation by deep learning algorithms
We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
d’Aquino, M. [verfasserIn] Perna, S. [verfasserIn] Serpico, C. [verfasserIn] Visone, C. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Physica / B - Amsterdam : Elsevier, 1988, 675 |
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| Übergeordnetes Werk: |
volume:675 |
| DOI / URN: |
10.1016/j.physb.2023.415596 |
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| Katalog-ID: |
ELV066524849 |
|---|
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| 245 | 1 | 0 | |a Hysteresis compensation by deep learning algorithms |
| 264 | 1 | |c 2023 | |
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| 520 | |a We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suitable to represent the classical operators of Preisach type, while the latter, showing clockwise cycles, is well behaved to describe the inverse or compensator operator. The paper is aimed to propose a unified approach that, through a specific structure of the hysteresis model (splitting the memory rules from the rest) is able to optimize performances of a Deep Learning (DL) algorithm and describe both hysteresis process and its compensator with good accuracy and computational efficiency. The approach would pander the actual stream that locates the need to extract the basic features of the phenomenon in connection to a neural algorithm in order to improve the modeling performances of the whole model, specifically aimed to implement compensator operators for e.g. control tasks. | ||
| 650 | 4 | |a Hysteresis | |
| 650 | 4 | |a Preisach operator | |
| 650 | 4 | |a Hysteresis compensation | |
| 650 | 4 | |a Machine learning | |
| 700 | 1 | |a Perna, S. |e verfasserin |4 aut | |
| 700 | 1 | |a Serpico, C. |e verfasserin |4 aut | |
| 700 | 1 | |a Visone, C. |e verfasserin |0 (orcid)0000-0001-8761-4192 |4 aut | |
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10.1016/j.physb.2023.415596 doi (DE-627)ELV066524849 (ELSEVIER)S0921-4526(23)00963-8 DE-627 ger DE-627 rda eng 530 VZ 33.60 bkl 51.00 bkl d’Aquino, M. verfasserin aut Hysteresis compensation by deep learning algorithms 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suitable to represent the classical operators of Preisach type, while the latter, showing clockwise cycles, is well behaved to describe the inverse or compensator operator. The paper is aimed to propose a unified approach that, through a specific structure of the hysteresis model (splitting the memory rules from the rest) is able to optimize performances of a Deep Learning (DL) algorithm and describe both hysteresis process and its compensator with good accuracy and computational efficiency. The approach would pander the actual stream that locates the need to extract the basic features of the phenomenon in connection to a neural algorithm in order to improve the modeling performances of the whole model, specifically aimed to implement compensator operators for e.g. control tasks. Hysteresis Preisach operator Hysteresis compensation Machine learning Perna, S. verfasserin aut Serpico, C. verfasserin aut Visone, C. verfasserin (orcid)0000-0001-8761-4192 aut Enthalten in Physica / B Amsterdam : Elsevier, 1988 675 Online-Ressource (DE-627)266015093 (DE-600)1466579-7 (DE-576)074959840 1873-2135 nnns volume:675 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.60 Kondensierte Materie: Allgemeines VZ 51.00 Werkstoffkunde: Allgemeines VZ AR 675 |
| spelling |
10.1016/j.physb.2023.415596 doi (DE-627)ELV066524849 (ELSEVIER)S0921-4526(23)00963-8 DE-627 ger DE-627 rda eng 530 VZ 33.60 bkl 51.00 bkl d’Aquino, M. verfasserin aut Hysteresis compensation by deep learning algorithms 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suitable to represent the classical operators of Preisach type, while the latter, showing clockwise cycles, is well behaved to describe the inverse or compensator operator. The paper is aimed to propose a unified approach that, through a specific structure of the hysteresis model (splitting the memory rules from the rest) is able to optimize performances of a Deep Learning (DL) algorithm and describe both hysteresis process and its compensator with good accuracy and computational efficiency. The approach would pander the actual stream that locates the need to extract the basic features of the phenomenon in connection to a neural algorithm in order to improve the modeling performances of the whole model, specifically aimed to implement compensator operators for e.g. control tasks. Hysteresis Preisach operator Hysteresis compensation Machine learning Perna, S. verfasserin aut Serpico, C. verfasserin aut Visone, C. verfasserin (orcid)0000-0001-8761-4192 aut Enthalten in Physica / B Amsterdam : Elsevier, 1988 675 Online-Ressource (DE-627)266015093 (DE-600)1466579-7 (DE-576)074959840 1873-2135 nnns volume:675 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.60 Kondensierte Materie: Allgemeines VZ 51.00 Werkstoffkunde: Allgemeines VZ AR 675 |
| allfields_unstemmed |
10.1016/j.physb.2023.415596 doi (DE-627)ELV066524849 (ELSEVIER)S0921-4526(23)00963-8 DE-627 ger DE-627 rda eng 530 VZ 33.60 bkl 51.00 bkl d’Aquino, M. verfasserin aut Hysteresis compensation by deep learning algorithms 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suitable to represent the classical operators of Preisach type, while the latter, showing clockwise cycles, is well behaved to describe the inverse or compensator operator. The paper is aimed to propose a unified approach that, through a specific structure of the hysteresis model (splitting the memory rules from the rest) is able to optimize performances of a Deep Learning (DL) algorithm and describe both hysteresis process and its compensator with good accuracy and computational efficiency. The approach would pander the actual stream that locates the need to extract the basic features of the phenomenon in connection to a neural algorithm in order to improve the modeling performances of the whole model, specifically aimed to implement compensator operators for e.g. control tasks. Hysteresis Preisach operator Hysteresis compensation Machine learning Perna, S. verfasserin aut Serpico, C. verfasserin aut Visone, C. verfasserin (orcid)0000-0001-8761-4192 aut Enthalten in Physica / B Amsterdam : Elsevier, 1988 675 Online-Ressource (DE-627)266015093 (DE-600)1466579-7 (DE-576)074959840 1873-2135 nnns volume:675 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.60 Kondensierte Materie: Allgemeines VZ 51.00 Werkstoffkunde: Allgemeines VZ AR 675 |
| allfieldsGer |
10.1016/j.physb.2023.415596 doi (DE-627)ELV066524849 (ELSEVIER)S0921-4526(23)00963-8 DE-627 ger DE-627 rda eng 530 VZ 33.60 bkl 51.00 bkl d’Aquino, M. verfasserin aut Hysteresis compensation by deep learning algorithms 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suitable to represent the classical operators of Preisach type, while the latter, showing clockwise cycles, is well behaved to describe the inverse or compensator operator. The paper is aimed to propose a unified approach that, through a specific structure of the hysteresis model (splitting the memory rules from the rest) is able to optimize performances of a Deep Learning (DL) algorithm and describe both hysteresis process and its compensator with good accuracy and computational efficiency. The approach would pander the actual stream that locates the need to extract the basic features of the phenomenon in connection to a neural algorithm in order to improve the modeling performances of the whole model, specifically aimed to implement compensator operators for e.g. control tasks. Hysteresis Preisach operator Hysteresis compensation Machine learning Perna, S. verfasserin aut Serpico, C. verfasserin aut Visone, C. verfasserin (orcid)0000-0001-8761-4192 aut Enthalten in Physica / B Amsterdam : Elsevier, 1988 675 Online-Ressource (DE-627)266015093 (DE-600)1466579-7 (DE-576)074959840 1873-2135 nnns volume:675 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.60 Kondensierte Materie: Allgemeines VZ 51.00 Werkstoffkunde: Allgemeines VZ AR 675 |
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Hysteresis compensation by deep learning algorithms |
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Hysteresis compensation by deep learning algorithms |
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d’Aquino, M. |
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d’Aquino, M. Perna, S. Serpico, C. Visone, C. |
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d’Aquino, M. |
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10.1016/j.physb.2023.415596 |
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hysteresis compensation by deep learning algorithms |
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Hysteresis compensation by deep learning algorithms |
| abstract |
We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suitable to represent the classical operators of Preisach type, while the latter, showing clockwise cycles, is well behaved to describe the inverse or compensator operator. The paper is aimed to propose a unified approach that, through a specific structure of the hysteresis model (splitting the memory rules from the rest) is able to optimize performances of a Deep Learning (DL) algorithm and describe both hysteresis process and its compensator with good accuracy and computational efficiency. The approach would pander the actual stream that locates the need to extract the basic features of the phenomenon in connection to a neural algorithm in order to improve the modeling performances of the whole model, specifically aimed to implement compensator operators for e.g. control tasks. |
| abstractGer |
We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suitable to represent the classical operators of Preisach type, while the latter, showing clockwise cycles, is well behaved to describe the inverse or compensator operator. The paper is aimed to propose a unified approach that, through a specific structure of the hysteresis model (splitting the memory rules from the rest) is able to optimize performances of a Deep Learning (DL) algorithm and describe both hysteresis process and its compensator with good accuracy and computational efficiency. The approach would pander the actual stream that locates the need to extract the basic features of the phenomenon in connection to a neural algorithm in order to improve the modeling performances of the whole model, specifically aimed to implement compensator operators for e.g. control tasks. |
| abstract_unstemmed |
We propose a Deep Learning (DL) algorithm, which connected to a Preisach-memory updating rule, allows to describe rate-independent memory phenomena. Memory rules could be realized through the employment of Play or Stop operators, which, as known, show counter- or clockwise loops. The former are suitable to represent the classical operators of Preisach type, while the latter, showing clockwise cycles, is well behaved to describe the inverse or compensator operator. The paper is aimed to propose a unified approach that, through a specific structure of the hysteresis model (splitting the memory rules from the rest) is able to optimize performances of a Deep Learning (DL) algorithm and describe both hysteresis process and its compensator with good accuracy and computational efficiency. The approach would pander the actual stream that locates the need to extract the basic features of the phenomenon in connection to a neural algorithm in order to improve the modeling performances of the whole model, specifically aimed to implement compensator operators for e.g. control tasks. |
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Hysteresis compensation by deep learning algorithms |
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