Approximation by Max-Product Neural Network Operators of Kantorovich Type
Abstract In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of Lp-approximation for not necessarily continuous data. Moreover, also the case of the pointwise and uniform approximation of cont...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Costarelli, Danilo [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer International Publishing 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Results in mathematics - Springer International Publishing, 1984, 69(2016), 3-4 vom: 06. Apr., Seite 505-519 |
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| Übergeordnetes Werk: |
volume:69 ; year:2016 ; number:3-4 ; day:06 ; month:04 ; pages:505-519 |
| Links: |
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| DOI / URN: |
10.1007/s00025-016-0546-7 |
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OLC2069535169 |
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| 520 | |a Abstract In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of Lp-approximation for not necessarily continuous data. Moreover, also the case of the pointwise and uniform approximation of continuous functions is considered. Finally, several examples of sigmoidal functions for which the above theory can be applied are presented. | ||
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10.1007/s00025-016-0546-7 doi (DE-627)OLC2069535169 (DE-He213)s00025-016-0546-7-p DE-627 ger DE-627 rakwb eng 510 VZ 510 VZ 17,1 ssgn Costarelli, Danilo verfasserin aut Approximation by Max-Product Neural Network Operators of Kantorovich Type 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer International Publishing 2016 Abstract In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of Lp-approximation for not necessarily continuous data. Moreover, also the case of the pointwise and uniform approximation of continuous functions is considered. Finally, several examples of sigmoidal functions for which the above theory can be applied are presented. Sigmoidal functions neural networks operators uniform approximation -approximation max-product operators Vinti, Gianluca aut Enthalten in Results in mathematics Springer International Publishing, 1984 69(2016), 3-4 vom: 06. Apr., Seite 505-519 (DE-627)129571423 (DE-600)226632-5 (DE-576)015060160 1422-6383 nnns volume:69 year:2016 number:3-4 day:06 month:04 pages:505-519 https://doi.org/10.1007/s00025-016-0546-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_22 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2088 GBV_ILN_4027 GBV_ILN_4277 GBV_ILN_4318 AR 69 2016 3-4 06 04 505-519 |
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10.1007/s00025-016-0546-7 doi (DE-627)OLC2069535169 (DE-He213)s00025-016-0546-7-p DE-627 ger DE-627 rakwb eng 510 VZ 510 VZ 17,1 ssgn Costarelli, Danilo verfasserin aut Approximation by Max-Product Neural Network Operators of Kantorovich Type 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer International Publishing 2016 Abstract In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of Lp-approximation for not necessarily continuous data. Moreover, also the case of the pointwise and uniform approximation of continuous functions is considered. Finally, several examples of sigmoidal functions for which the above theory can be applied are presented. Sigmoidal functions neural networks operators uniform approximation -approximation max-product operators Vinti, Gianluca aut Enthalten in Results in mathematics Springer International Publishing, 1984 69(2016), 3-4 vom: 06. Apr., Seite 505-519 (DE-627)129571423 (DE-600)226632-5 (DE-576)015060160 1422-6383 nnns volume:69 year:2016 number:3-4 day:06 month:04 pages:505-519 https://doi.org/10.1007/s00025-016-0546-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_22 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2088 GBV_ILN_4027 GBV_ILN_4277 GBV_ILN_4318 AR 69 2016 3-4 06 04 505-519 |
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Abstract In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of Lp-approximation for not necessarily continuous data. Moreover, also the case of the pointwise and uniform approximation of continuous functions is considered. Finally, several examples of sigmoidal functions for which the above theory can be applied are presented. © Springer International Publishing 2016 |
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Abstract In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of Lp-approximation for not necessarily continuous data. Moreover, also the case of the pointwise and uniform approximation of continuous functions is considered. Finally, several examples of sigmoidal functions for which the above theory can be applied are presented. © Springer International Publishing 2016 |
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Abstract In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of Lp-approximation for not necessarily continuous data. Moreover, also the case of the pointwise and uniform approximation of continuous functions is considered. Finally, several examples of sigmoidal functions for which the above theory can be applied are presented. © Springer International Publishing 2016 |
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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">OLC2069535169</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230323113436.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00025-016-0546-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2069535169</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00025-016-0546-7-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">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">17,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Costarelli, Danilo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Approximation by Max-Product Neural Network Operators of Kantorovich Type</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</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 International Publishing 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of Lp-approximation for not necessarily continuous data. 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Finally, several examples of sigmoidal functions for which the above theory can be applied are presented.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sigmoidal functions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural networks operators</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">uniform approximation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">-approximation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">max-product operators</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vinti, Gianluca</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Results in mathematics</subfield><subfield code="d">Springer International Publishing, 1984</subfield><subfield code="g">69(2016), 3-4 vom: 06. 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