Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix
Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost functi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ahn, Choon Ki [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
Model predictive stabilization |
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| Anmerkung: |
© Springer-Verlag London 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 23(2013), Suppl 1 vom: 27. März, Seite 271-277 |
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| Übergeordnetes Werk: |
volume:23 ; year:2013 ; number:Suppl 1 ; day:27 ; month:03 ; pages:271-277 |
| Links: |
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| DOI / URN: |
10.1007/s00521-013-1381-3 |
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OLC2025589069 |
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| 520 | |a Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. | ||
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| 700 | 1 | |a Lim, Myo Taeg |4 aut | |
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10.1007/s00521-013-1381-3 doi (DE-627)OLC2025589069 (DE-He213)s00521-013-1381-3-p DE-627 ger DE-627 rakwb eng 004 VZ Ahn, Choon Ki verfasserin aut Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. Model predictive stabilization Takagi–Sugeno (T–S) fuzzy neural networks Cost monotonicity Linear matrix inequality (LMI) Lim, Myo Taeg aut Enthalten in Neural computing & applications Springer London, 1993 23(2013), Suppl 1 vom: 27. März, Seite 271-277 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2013 number:Suppl 1 day:27 month:03 pages:271-277 https://doi.org/10.1007/s00521-013-1381-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2013 Suppl 1 27 03 271-277 |
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10.1007/s00521-013-1381-3 doi (DE-627)OLC2025589069 (DE-He213)s00521-013-1381-3-p DE-627 ger DE-627 rakwb eng 004 VZ Ahn, Choon Ki verfasserin aut Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. Model predictive stabilization Takagi–Sugeno (T–S) fuzzy neural networks Cost monotonicity Linear matrix inequality (LMI) Lim, Myo Taeg aut Enthalten in Neural computing & applications Springer London, 1993 23(2013), Suppl 1 vom: 27. März, Seite 271-277 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2013 number:Suppl 1 day:27 month:03 pages:271-277 https://doi.org/10.1007/s00521-013-1381-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2013 Suppl 1 27 03 271-277 |
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10.1007/s00521-013-1381-3 doi (DE-627)OLC2025589069 (DE-He213)s00521-013-1381-3-p DE-627 ger DE-627 rakwb eng 004 VZ Ahn, Choon Ki verfasserin aut Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. Model predictive stabilization Takagi–Sugeno (T–S) fuzzy neural networks Cost monotonicity Linear matrix inequality (LMI) Lim, Myo Taeg aut Enthalten in Neural computing & applications Springer London, 1993 23(2013), Suppl 1 vom: 27. März, Seite 271-277 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2013 number:Suppl 1 day:27 month:03 pages:271-277 https://doi.org/10.1007/s00521-013-1381-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2013 Suppl 1 27 03 271-277 |
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10.1007/s00521-013-1381-3 doi (DE-627)OLC2025589069 (DE-He213)s00521-013-1381-3-p DE-627 ger DE-627 rakwb eng 004 VZ Ahn, Choon Ki verfasserin aut Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. Model predictive stabilization Takagi–Sugeno (T–S) fuzzy neural networks Cost monotonicity Linear matrix inequality (LMI) Lim, Myo Taeg aut Enthalten in Neural computing & applications Springer London, 1993 23(2013), Suppl 1 vom: 27. März, Seite 271-277 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:23 year:2013 number:Suppl 1 day:27 month:03 pages:271-277 https://doi.org/10.1007/s00521-013-1381-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 23 2013 Suppl 1 27 03 271-277 |
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Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. © Springer-Verlag London 2013 |
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Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. © Springer-Verlag London 2013 |
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Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme. © Springer-Verlag London 2013 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025589069</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114544.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2013 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-013-1381-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025589069</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-013-1381-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ahn, Choon Ki</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London 2013</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper investigates the model predictive stabilization problem for Takagi–Sugeno (T–S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T–S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model predictive stabilization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Takagi–Sugeno (T–S) fuzzy neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cost monotonicity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Linear matrix inequality (LMI)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lim, Myo Taeg</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">23(2013), Suppl 1 vom: 27. März, Seite 271-277</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2013</subfield><subfield code="g">number:Suppl 1</subfield><subfield code="g">day:27</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:271-277</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-013-1381-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2013</subfield><subfield code="e">Suppl 1</subfield><subfield code="b">27</subfield><subfield code="c">03</subfield><subfield code="h">271-277</subfield></datafield></record></collection>
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