A Novel Adaptive Kernel for the RBF Neural Networks

Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the p...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Khan, Shujaat [verfasserIn] Naseem, Imran Togneri, Roberto Bennamoun, Mohammed

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Artificial neural networks Radial basis function Gaussian kernel Support vector machine Euclidean distance Cosine distance Kernel fusion

Anmerkung:	© Springer Science+Business Media New York 2016

Übergeordnetes Werk:	Enthalten in: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 36(2016), 4 vom: 30. Juli, Seite 1639-1653
Übergeordnetes Werk:	volume:36 ; year:2016 ; number:4 ; day:30 ; month:07 ; pages:1639-1653

Links:	Volltext

DOI / URN:	10.1007/s00034-016-0375-7

Katalog-ID:	SPR000559482

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245	1	2	\|a A Novel Adaptive Kernel for the RBF Neural Networks
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520			\|a Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation.
650		4	\|a Artificial neural networks \|7 (dpeaa)DE-He213
650		4	\|a Radial basis function \|7 (dpeaa)DE-He213
650		4	\|a Gaussian kernel \|7 (dpeaa)DE-He213
650		4	\|a Support vector machine \|7 (dpeaa)DE-He213
650		4	\|a Euclidean distance \|7 (dpeaa)DE-He213
650		4	\|a Cosine distance \|7 (dpeaa)DE-He213
650		4	\|a Kernel fusion \|7 (dpeaa)DE-He213
700	1		\|a Naseem, Imran \|4 aut
700	1		\|a Togneri, Roberto \|4 aut
700	1		\|a Bennamoun, Mohammed \|4 aut
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856	4	0	\|u https://dx.doi.org/10.1007/s00034-016-0375-7 \|z lizenzpflichtig \|3 Volltext
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912			\|a GBV_ILN_602
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publishDate	2016
allfields	10.1007/s00034-016-0375-7 doi (DE-627)SPR000559482 (SPR)s00034-016-0375-7-e DE-627 ger DE-627 rakwb eng Khan, Shujaat verfasserin aut A Novel Adaptive Kernel for the RBF Neural Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation. Artificial neural networks (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Gaussian kernel (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance (dpeaa)DE-He213 Cosine distance (dpeaa)DE-He213 Kernel fusion (dpeaa)DE-He213 Naseem, Imran aut Togneri, Roberto aut Bennamoun, Mohammed aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 4 vom: 30. Juli, Seite 1639-1653 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:4 day:30 month:07 pages:1639-1653 https://dx.doi.org/10.1007/s00034-016-0375-7 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_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_267 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 4 30 07 1639-1653
spelling	10.1007/s00034-016-0375-7 doi (DE-627)SPR000559482 (SPR)s00034-016-0375-7-e DE-627 ger DE-627 rakwb eng Khan, Shujaat verfasserin aut A Novel Adaptive Kernel for the RBF Neural Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation. Artificial neural networks (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Gaussian kernel (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance (dpeaa)DE-He213 Cosine distance (dpeaa)DE-He213 Kernel fusion (dpeaa)DE-He213 Naseem, Imran aut Togneri, Roberto aut Bennamoun, Mohammed aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 4 vom: 30. Juli, Seite 1639-1653 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:4 day:30 month:07 pages:1639-1653 https://dx.doi.org/10.1007/s00034-016-0375-7 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_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_267 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 4 30 07 1639-1653
allfields_unstemmed	10.1007/s00034-016-0375-7 doi (DE-627)SPR000559482 (SPR)s00034-016-0375-7-e DE-627 ger DE-627 rakwb eng Khan, Shujaat verfasserin aut A Novel Adaptive Kernel for the RBF Neural Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation. Artificial neural networks (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Gaussian kernel (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance (dpeaa)DE-He213 Cosine distance (dpeaa)DE-He213 Kernel fusion (dpeaa)DE-He213 Naseem, Imran aut Togneri, Roberto aut Bennamoun, Mohammed aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 4 vom: 30. Juli, Seite 1639-1653 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:4 day:30 month:07 pages:1639-1653 https://dx.doi.org/10.1007/s00034-016-0375-7 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_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_267 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 4 30 07 1639-1653
allfieldsGer	10.1007/s00034-016-0375-7 doi (DE-627)SPR000559482 (SPR)s00034-016-0375-7-e DE-627 ger DE-627 rakwb eng Khan, Shujaat verfasserin aut A Novel Adaptive Kernel for the RBF Neural Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation. Artificial neural networks (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Gaussian kernel (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance (dpeaa)DE-He213 Cosine distance (dpeaa)DE-He213 Kernel fusion (dpeaa)DE-He213 Naseem, Imran aut Togneri, Roberto aut Bennamoun, Mohammed aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 4 vom: 30. Juli, Seite 1639-1653 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:4 day:30 month:07 pages:1639-1653 https://dx.doi.org/10.1007/s00034-016-0375-7 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_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_267 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 4 30 07 1639-1653
allfieldsSound	10.1007/s00034-016-0375-7 doi (DE-627)SPR000559482 (SPR)s00034-016-0375-7-e DE-627 ger DE-627 rakwb eng Khan, Shujaat verfasserin aut A Novel Adaptive Kernel for the RBF Neural Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation. Artificial neural networks (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Gaussian kernel (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance (dpeaa)DE-He213 Cosine distance (dpeaa)DE-He213 Kernel fusion (dpeaa)DE-He213 Naseem, Imran aut Togneri, Roberto aut Bennamoun, Mohammed aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 4 vom: 30. Juli, Seite 1639-1653 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:4 day:30 month:07 pages:1639-1653 https://dx.doi.org/10.1007/s00034-016-0375-7 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_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_267 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 4 30 07 1639-1653
language	English
source	Enthalten in Circuits, systems and signal processing 36(2016), 4 vom: 30. Juli, Seite 1639-1653 volume:36 year:2016 number:4 day:30 month:07 pages:1639-1653
sourceStr	Enthalten in Circuits, systems and signal processing 36(2016), 4 vom: 30. Juli, Seite 1639-1653 volume:36 year:2016 number:4 day:30 month:07 pages:1639-1653
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial neural networks Radial basis function Gaussian kernel Support vector machine Euclidean distance Cosine distance Kernel fusion
isfreeaccess_bool	false
container_title	Circuits, systems and signal processing
authorswithroles_txt_mv	Khan, Shujaat @@aut@@ Naseem, Imran @@aut@@ Togneri, Roberto @@aut@@ Bennamoun, Mohammed @@aut@@
publishDateDaySort_date	2016-07-30T00:00:00Z
hierarchy_top_id	351975470
id	SPR000559482
language_de	englisch
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author	Khan, Shujaat
spellingShingle	Khan, Shujaat misc Artificial neural networks misc Radial basis function misc Gaussian kernel misc Support vector machine misc Euclidean distance misc Cosine distance misc Kernel fusion A Novel Adaptive Kernel for the RBF Neural Networks
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topic_title	A Novel Adaptive Kernel for the RBF Neural Networks Artificial neural networks (dpeaa)DE-He213 Radial basis function (dpeaa)DE-He213 Gaussian kernel (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance (dpeaa)DE-He213 Cosine distance (dpeaa)DE-He213 Kernel fusion (dpeaa)DE-He213
topic	misc Artificial neural networks misc Radial basis function misc Gaussian kernel misc Support vector machine misc Euclidean distance misc Cosine distance misc Kernel fusion
topic_unstemmed	misc Artificial neural networks misc Radial basis function misc Gaussian kernel misc Support vector machine misc Euclidean distance misc Cosine distance misc Kernel fusion
topic_browse	misc Artificial neural networks misc Radial basis function misc Gaussian kernel misc Support vector machine misc Euclidean distance misc Cosine distance misc Kernel fusion
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	A Novel Adaptive Kernel for the RBF Neural Networks
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title_full	A Novel Adaptive Kernel for the RBF Neural Networks
author_sort	Khan, Shujaat
journal	Circuits, systems and signal processing
journalStr	Circuits, systems and signal processing
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author_browse	Khan, Shujaat Naseem, Imran Togneri, Roberto Bennamoun, Mohammed
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author-letter	Khan, Shujaat
doi_str_mv	10.1007/s00034-016-0375-7
title_sort	novel adaptive kernel for the rbf neural networks
title_auth	A Novel Adaptive Kernel for the RBF Neural Networks
abstract	Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation. © Springer Science+Business Media New York 2016
abstractGer	Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation. © Springer Science+Business Media New York 2016
abstract_unstemmed	Abstract In this paper, we propose a novel adaptive kernel for the radial basis function neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method, thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation, namely nonlinear system identification, patter classification and function approximation. © Springer Science+Business Media New York 2016
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title_short	A Novel Adaptive Kernel for the RBF Neural Networks
url	https://dx.doi.org/10.1007/s00034-016-0375-7
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author2	Naseem, Imran Togneri, Roberto Bennamoun, Mohammed
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doi_str	10.1007/s00034-016-0375-7
up_date	2024-07-03T16:54:41.057Z
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A Novel Adaptive Kernel for the RBF Neural Networks

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