Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC
Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inp...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rodriguez, Floriberto Ortiz [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC. 2008 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 28(2008), 1 vom: 10. Juli, Seite 49-62 |
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| Übergeordnetes Werk: |
volume:28 ; year:2008 ; number:1 ; day:10 ; month:07 ; pages:49-62 |
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| DOI / URN: |
10.1007/s11063-008-9081-1 |
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| 520 | |a Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven. | ||
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10.1007/s11063-008-9081-1 doi (DE-627)OLC2044705885 (DE-He213)s11063-008-9081-1-p DE-627 ger DE-627 rakwb eng 000 VZ Rodriguez, Floriberto Ortiz verfasserin aut Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC. 2008 Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven. Fuzzy CMAC Modeling Recurrent neural networks Yu, Wen aut Moreno-Armendariz, Marco A. aut Enthalten in Neural processing letters Springer US, 1994 28(2008), 1 vom: 10. Juli, Seite 49-62 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:28 year:2008 number:1 day:10 month:07 pages:49-62 https://doi.org/10.1007/s11063-008-9081-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 GBV_ILN_120 GBV_ILN_2021 AR 28 2008 1 10 07 49-62 |
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10.1007/s11063-008-9081-1 doi (DE-627)OLC2044705885 (DE-He213)s11063-008-9081-1-p DE-627 ger DE-627 rakwb eng 000 VZ Rodriguez, Floriberto Ortiz verfasserin aut Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC. 2008 Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven. Fuzzy CMAC Modeling Recurrent neural networks Yu, Wen aut Moreno-Armendariz, Marco A. aut Enthalten in Neural processing letters Springer US, 1994 28(2008), 1 vom: 10. Juli, Seite 49-62 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:28 year:2008 number:1 day:10 month:07 pages:49-62 https://doi.org/10.1007/s11063-008-9081-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 GBV_ILN_120 GBV_ILN_2021 AR 28 2008 1 10 07 49-62 |
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10.1007/s11063-008-9081-1 doi (DE-627)OLC2044705885 (DE-He213)s11063-008-9081-1-p DE-627 ger DE-627 rakwb eng 000 VZ Rodriguez, Floriberto Ortiz verfasserin aut Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC. 2008 Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven. Fuzzy CMAC Modeling Recurrent neural networks Yu, Wen aut Moreno-Armendariz, Marco A. aut Enthalten in Neural processing letters Springer US, 1994 28(2008), 1 vom: 10. Juli, Seite 49-62 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:28 year:2008 number:1 day:10 month:07 pages:49-62 https://doi.org/10.1007/s11063-008-9081-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 GBV_ILN_120 GBV_ILN_2021 AR 28 2008 1 10 07 49-62 |
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10.1007/s11063-008-9081-1 doi (DE-627)OLC2044705885 (DE-He213)s11063-008-9081-1-p DE-627 ger DE-627 rakwb eng 000 VZ Rodriguez, Floriberto Ortiz verfasserin aut Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC. 2008 Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven. Fuzzy CMAC Modeling Recurrent neural networks Yu, Wen aut Moreno-Armendariz, Marco A. aut Enthalten in Neural processing letters Springer US, 1994 28(2008), 1 vom: 10. Juli, Seite 49-62 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:28 year:2008 number:1 day:10 month:07 pages:49-62 https://doi.org/10.1007/s11063-008-9081-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 GBV_ILN_120 GBV_ILN_2021 AR 28 2008 1 10 07 49-62 |
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Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven. © Springer Science+Business Media, LLC. 2008 |
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Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven. © Springer Science+Business Media, LLC. 2008 |
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Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven. © Springer Science+Business Media, LLC. 2008 |
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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">OLC2044705885</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503210124.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2008 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-008-9081-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2044705885</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11063-008-9081-1-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">000</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rodriguez, Floriberto Ortiz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</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 Science+Business Media, LLC. 2008</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). 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