Artificial Neural Networks and Logic Circuit Synthesis

We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algori...
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Autor*in:	Gurov, S. I. [verfasserIn] Zolotarev, D. V. Samburskii, A. I.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	recursive neural networks logic circuit synthesis majority logic

Anmerkung:	© Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Computational mathematics and modeling - New York, NY [u.a.] : Consultants Bureau, 1990, 32(2021), 4 vom: Okt., Seite 490-499
Übergeordnetes Werk:	volume:32 ; year:2021 ; number:4 ; month:10 ; pages:490-499

Links:	Volltext

DOI / URN:	10.1007/s10598-022-09548-z

Katalog-ID:	SPR046977635

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allfields	10.1007/s10598-022-09548-z doi (DE-627)SPR046977635 (SPR)s10598-022-09548-z-e DE-627 ger DE-627 rakwb eng Gurov, S. I. verfasserin aut Artificial Neural Networks and Logic Circuit Synthesis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2022 We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization. recursive neural networks (dpeaa)DE-He213 logic circuit synthesis (dpeaa)DE-He213 majority logic (dpeaa)DE-He213 Zolotarev, D. V. aut Samburskii, A. I. aut Enthalten in Computational mathematics and modeling New York, NY [u.a.] : Consultants Bureau, 1990 32(2021), 4 vom: Okt., Seite 490-499 (DE-627)325568634 (DE-600)2037135-4 1573-837X nnns volume:32 year:2021 number:4 month:10 pages:490-499 https://dx.doi.org/10.1007/s10598-022-09548-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2021 4 10 490-499
spelling	10.1007/s10598-022-09548-z doi (DE-627)SPR046977635 (SPR)s10598-022-09548-z-e DE-627 ger DE-627 rakwb eng Gurov, S. I. verfasserin aut Artificial Neural Networks and Logic Circuit Synthesis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2022 We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization. recursive neural networks (dpeaa)DE-He213 logic circuit synthesis (dpeaa)DE-He213 majority logic (dpeaa)DE-He213 Zolotarev, D. V. aut Samburskii, A. I. aut Enthalten in Computational mathematics and modeling New York, NY [u.a.] : Consultants Bureau, 1990 32(2021), 4 vom: Okt., Seite 490-499 (DE-627)325568634 (DE-600)2037135-4 1573-837X nnns volume:32 year:2021 number:4 month:10 pages:490-499 https://dx.doi.org/10.1007/s10598-022-09548-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2021 4 10 490-499
allfields_unstemmed	10.1007/s10598-022-09548-z doi (DE-627)SPR046977635 (SPR)s10598-022-09548-z-e DE-627 ger DE-627 rakwb eng Gurov, S. I. verfasserin aut Artificial Neural Networks and Logic Circuit Synthesis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2022 We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization. recursive neural networks (dpeaa)DE-He213 logic circuit synthesis (dpeaa)DE-He213 majority logic (dpeaa)DE-He213 Zolotarev, D. V. aut Samburskii, A. I. aut Enthalten in Computational mathematics and modeling New York, NY [u.a.] : Consultants Bureau, 1990 32(2021), 4 vom: Okt., Seite 490-499 (DE-627)325568634 (DE-600)2037135-4 1573-837X nnns volume:32 year:2021 number:4 month:10 pages:490-499 https://dx.doi.org/10.1007/s10598-022-09548-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2021 4 10 490-499
allfieldsGer	10.1007/s10598-022-09548-z doi (DE-627)SPR046977635 (SPR)s10598-022-09548-z-e DE-627 ger DE-627 rakwb eng Gurov, S. I. verfasserin aut Artificial Neural Networks and Logic Circuit Synthesis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2022 We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization. recursive neural networks (dpeaa)DE-He213 logic circuit synthesis (dpeaa)DE-He213 majority logic (dpeaa)DE-He213 Zolotarev, D. V. aut Samburskii, A. I. aut Enthalten in Computational mathematics and modeling New York, NY [u.a.] : Consultants Bureau, 1990 32(2021), 4 vom: Okt., Seite 490-499 (DE-627)325568634 (DE-600)2037135-4 1573-837X nnns volume:32 year:2021 number:4 month:10 pages:490-499 https://dx.doi.org/10.1007/s10598-022-09548-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2021 4 10 490-499
allfieldsSound	10.1007/s10598-022-09548-z doi (DE-627)SPR046977635 (SPR)s10598-022-09548-z-e DE-627 ger DE-627 rakwb eng Gurov, S. I. verfasserin aut Artificial Neural Networks and Logic Circuit Synthesis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2022 We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization. recursive neural networks (dpeaa)DE-He213 logic circuit synthesis (dpeaa)DE-He213 majority logic (dpeaa)DE-He213 Zolotarev, D. V. aut Samburskii, A. I. aut Enthalten in Computational mathematics and modeling New York, NY [u.a.] : Consultants Bureau, 1990 32(2021), 4 vom: Okt., Seite 490-499 (DE-627)325568634 (DE-600)2037135-4 1573-837X nnns volume:32 year:2021 number:4 month:10 pages:490-499 https://dx.doi.org/10.1007/s10598-022-09548-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2021 4 10 490-499
language	English
source	Enthalten in Computational mathematics and modeling 32(2021), 4 vom: Okt., Seite 490-499 volume:32 year:2021 number:4 month:10 pages:490-499
sourceStr	Enthalten in Computational mathematics and modeling 32(2021), 4 vom: Okt., Seite 490-499 volume:32 year:2021 number:4 month:10 pages:490-499
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	recursive neural networks logic circuit synthesis majority logic
isfreeaccess_bool	false
container_title	Computational mathematics and modeling
authorswithroles_txt_mv	Gurov, S. I. @@aut@@ Zolotarev, D. V. @@aut@@ Samburskii, A. I. @@aut@@
publishDateDaySort_date	2021-10-01T00:00:00Z
hierarchy_top_id	325568634
id	SPR046977635
language_de	englisch
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title	Artificial Neural Networks and Logic Circuit Synthesis
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title_auth	Artificial Neural Networks and Logic Circuit Synthesis
abstract	We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization. © Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization. © Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	We consider the application of artificial neural networks to logic circuit synthesis. The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization. © Springer Science+Business Media, LLC, part of Springer Nature 2022
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title_short	Artificial Neural Networks and Logic Circuit Synthesis
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The potential promise of this method is shown. Circuit optimization in a majority logic basis is demonstrated. A method is proposed for the application of recursive neural networks for the construction of an algorithm for logiccircuit synthesis and size optimization.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">recursive neural networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">logic circuit synthesis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">majority logic</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zolotarev, D. V.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Samburskii, A. I.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational mathematics and modeling</subfield><subfield code="d">New York, NY [u.a.] : Consultants Bureau, 1990</subfield><subfield code="g">32(2021), 4 vom: Okt., Seite 490-499</subfield><subfield code="w">(DE-627)325568634</subfield><subfield code="w">(DE-600)2037135-4</subfield><subfield code="x">1573-837X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:4</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:490-499</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10598-022-09548-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield 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