A self-evolving functional-linked wavelet neural network for control applications
Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hsu, Chun-Fei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
Functional-linked neural network |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Atomic collapse in graphene quantum dots in a magnetic field - Eren, I. ELSEVIER, 2022, the official journal of the World Federation on Soft Computing (WFSC), Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:13 ; year:2013 ; number:11 ; pages:4392-4402 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.asoc.2013.06.012 |
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ELV027126145 |
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| 520 | |a Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. | ||
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10.1016/j.asoc.2013.06.012 doi GBVA2013006000017.pica (DE-627)ELV027126145 (ELSEVIER)S1568-4946(13)00201-9 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 530 VZ 33.00 bkl Hsu, Chun-Fei verfasserin aut A self-evolving functional-linked wavelet neural network for control applications 2013 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. Neural control Elsevier Functional-linked neural network Elsevier Wavelet neural network, Dynamical structure Elsevier Adaptive control Elsevier Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:13 year:2013 number:11 pages:4392-4402 extent:11 https://doi.org/10.1016/j.asoc.2013.06.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 13 2013 11 4392-4402 11 045F 004 |
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10.1016/j.asoc.2013.06.012 doi GBVA2013006000017.pica (DE-627)ELV027126145 (ELSEVIER)S1568-4946(13)00201-9 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 530 VZ 33.00 bkl Hsu, Chun-Fei verfasserin aut A self-evolving functional-linked wavelet neural network for control applications 2013 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. Neural control Elsevier Functional-linked neural network Elsevier Wavelet neural network, Dynamical structure Elsevier Adaptive control Elsevier Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:13 year:2013 number:11 pages:4392-4402 extent:11 https://doi.org/10.1016/j.asoc.2013.06.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 13 2013 11 4392-4402 11 045F 004 |
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10.1016/j.asoc.2013.06.012 doi GBVA2013006000017.pica (DE-627)ELV027126145 (ELSEVIER)S1568-4946(13)00201-9 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 530 VZ 33.00 bkl Hsu, Chun-Fei verfasserin aut A self-evolving functional-linked wavelet neural network for control applications 2013 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. Neural control Elsevier Functional-linked neural network Elsevier Wavelet neural network, Dynamical structure Elsevier Adaptive control Elsevier Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:13 year:2013 number:11 pages:4392-4402 extent:11 https://doi.org/10.1016/j.asoc.2013.06.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 13 2013 11 4392-4402 11 045F 004 |
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10.1016/j.asoc.2013.06.012 doi GBVA2013006000017.pica (DE-627)ELV027126145 (ELSEVIER)S1568-4946(13)00201-9 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 530 VZ 33.00 bkl Hsu, Chun-Fei verfasserin aut A self-evolving functional-linked wavelet neural network for control applications 2013 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. Neural control Elsevier Functional-linked neural network Elsevier Wavelet neural network, Dynamical structure Elsevier Adaptive control Elsevier Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:13 year:2013 number:11 pages:4392-4402 extent:11 https://doi.org/10.1016/j.asoc.2013.06.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 13 2013 11 4392-4402 11 045F 004 |
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Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. |
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Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. |
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Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. |
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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">ELV027126145</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230623185426.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.asoc.2013.06.012</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2013006000017.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV027126145</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1568-4946(13)00201-9</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=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">33.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hsu, Chun-Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A self-evolving functional-linked wavelet neural network for control applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Highlights • The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. • This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. • The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. • Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Neural control</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Functional-linked neural network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Wavelet neural network, Dynamical structure</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Adaptive control</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Eren, I. 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