HEMT noise neural model based on bias conditions
Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues'...
Ausführliche Beschreibung
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| Autor*in: |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2004 |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Reproduktion: |
Emerald Fulltext Archive Database 1994-2005 |
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| Übergeordnetes Werk: |
In: Compel - Bradford : Emerald, 1982, 23(2004), 2, Seite 426-435 |
| Übergeordnetes Werk: |
volume:23 ; year:2004 ; number:2 ; pages:426-435 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1108/03321640410510587 |
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NLEJ219835012 |
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| 520 | |a Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. | ||
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10.1108/03321640410510587 doi (DE-627)NLEJ219835012 DE-627 ger DE-627 rakwb eng XA-GB HEMT noise neural model based on bias conditions 2004 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. Emerald Fulltext Archive Database 1994-2005 Microwave transistors Microwaves Neural net devices Neural nets Noise Markovic, Vera oth Marinkovic, Zlatica oth In Compel Bradford : Emerald, 1982 23(2004), 2, Seite 426-435 Online-Ressource (DE-627)NLEJ219579113 (DE-600)1501321-2 nnns volume:23 year:2004 number:2 pages:426-435 extent:10 http://dx.doi.org/10.1108/03321640410510587 GBV_USEFLAG_U ZDB-1-EFD GBV_NL_ARTICLE AR 23 2004 2 426-435 10 |
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10.1108/03321640410510587 doi (DE-627)NLEJ219835012 DE-627 ger DE-627 rakwb eng XA-GB HEMT noise neural model based on bias conditions 2004 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. Emerald Fulltext Archive Database 1994-2005 Microwave transistors Microwaves Neural net devices Neural nets Noise Markovic, Vera oth Marinkovic, Zlatica oth In Compel Bradford : Emerald, 1982 23(2004), 2, Seite 426-435 Online-Ressource (DE-627)NLEJ219579113 (DE-600)1501321-2 nnns volume:23 year:2004 number:2 pages:426-435 extent:10 http://dx.doi.org/10.1108/03321640410510587 GBV_USEFLAG_U ZDB-1-EFD GBV_NL_ARTICLE AR 23 2004 2 426-435 10 |
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10.1108/03321640410510587 doi (DE-627)NLEJ219835012 DE-627 ger DE-627 rakwb eng XA-GB HEMT noise neural model based on bias conditions 2004 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. Emerald Fulltext Archive Database 1994-2005 Microwave transistors Microwaves Neural net devices Neural nets Noise Markovic, Vera oth Marinkovic, Zlatica oth In Compel Bradford : Emerald, 1982 23(2004), 2, Seite 426-435 Online-Ressource (DE-627)NLEJ219579113 (DE-600)1501321-2 nnns volume:23 year:2004 number:2 pages:426-435 extent:10 http://dx.doi.org/10.1108/03321640410510587 GBV_USEFLAG_U ZDB-1-EFD GBV_NL_ARTICLE AR 23 2004 2 426-435 10 |
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10.1108/03321640410510587 doi (DE-627)NLEJ219835012 DE-627 ger DE-627 rakwb eng XA-GB HEMT noise neural model based on bias conditions 2004 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. Emerald Fulltext Archive Database 1994-2005 Microwave transistors Microwaves Neural net devices Neural nets Noise Markovic, Vera oth Marinkovic, Zlatica oth In Compel Bradford : Emerald, 1982 23(2004), 2, Seite 426-435 Online-Ressource (DE-627)NLEJ219579113 (DE-600)1501321-2 nnns volume:23 year:2004 number:2 pages:426-435 extent:10 http://dx.doi.org/10.1108/03321640410510587 GBV_USEFLAG_U ZDB-1-EFD GBV_NL_ARTICLE AR 23 2004 2 426-435 10 |
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10.1108/03321640410510587 doi (DE-627)NLEJ219835012 DE-627 ger DE-627 rakwb eng XA-GB HEMT noise neural model based on bias conditions 2004 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. Emerald Fulltext Archive Database 1994-2005 Microwave transistors Microwaves Neural net devices Neural nets Noise Markovic, Vera oth Marinkovic, Zlatica oth In Compel Bradford : Emerald, 1982 23(2004), 2, Seite 426-435 Online-Ressource (DE-627)NLEJ219579113 (DE-600)1501321-2 nnns volume:23 year:2004 number:2 pages:426-435 extent:10 http://dx.doi.org/10.1108/03321640410510587 GBV_USEFLAG_U ZDB-1-EFD GBV_NL_ARTICLE AR 23 2004 2 426-435 10 |
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hemt noise neural model based on bias conditions |
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HEMT noise neural model based on bias conditions |
| abstract |
Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. |
| abstractGer |
Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. |
| abstract_unstemmed |
Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure. |
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HEMT noise neural model based on bias conditions |
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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">NLEJ219835012</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707093835.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">090811s2004 xxk|||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1108/03321640410510587</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ219835012</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="044" ind1=" " ind2=" "><subfield code="c">XA-GB</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">HEMT noise neural model based on bias conditions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2004</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">10</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">Knowledge of the microwave transistor parameters at various bias conditions is often required in computer-aided design of complex microwave low-noise circuit. Since the measurements of noise parameters are very complex and time-consuming, microwave circuit designers usually use the catalogues' data or noise models. The noise data that can be found in the catalogues are often limited to a few frequencies and to one or few bias points. Further, most of the existing noise models require recalculation of elements/parameters of an equivalent circuit for every bias point. Microwave HEMT transistor noise prediction based on a multilayer perceptron neural network, proposed in this paper, enables noise prediction for all operating points over a wide frequency range. Neural networks are trained to learn noise parameters' dependence on bias conditions and frequency. After network training, noise prediction for a specified bias point requires only a network response calculation without changes in the network structure.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">Emerald Fulltext Archive Database 1994-2005</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Microwave transistors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Microwaves</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural net devices</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural nets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Noise</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Markovic, Vera</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Marinkovic, Zlatica</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Compel</subfield><subfield code="d">Bradford : Emerald, 1982</subfield><subfield code="g">23(2004), 2, Seite 426-435</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ219579113</subfield><subfield code="w">(DE-600)1501321-2</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2004</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:426-435</subfield><subfield code="g">extent:10</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1108/03321640410510587</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-EFD</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</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">2004</subfield><subfield code="e">2</subfield><subfield code="h">426-435</subfield><subfield code="g">10</subfield></datafield></record></collection>
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