Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks

In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These f...
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Autor*in:	Mishra, Akash [verfasserIn] Chandra, Pravin [verfasserIn] Ghose, Udayan [verfasserIn] Sodhi, Sartaj Singh [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Activation function adaptation Bi-modal derivative activation function Activation function Resilient backpropagation algorithm Sigmoidal feed-forward artificial neural network

Übergeordnetes Werk:	Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 61, Seite 983-994
Übergeordnetes Werk:	volume:61 ; pages:983-994

DOI / URN:	10.1016/j.asoc.2017.09.002

Katalog-ID:	ELV000847445

Internformat


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520			\|a In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes.
650		4	\|a Activation function adaptation
650		4	\|a Bi-modal derivative activation function
650		4	\|a Activation function
650		4	\|a Resilient backpropagation algorithm
650		4	\|a Sigmoidal feed-forward artificial neural network
700	1		\|a Chandra, Pravin \|e verfasserin \|4 aut
700	1		\|a Ghose, Udayan \|e verfasserin \|4 aut
700	1		\|a Sodhi, Sartaj Singh \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Applied soft computing \|d Amsterdam [u.a.] : Elsevier Science, 2001 \|g 61, Seite 983-994 \|h Online-Ressource \|w (DE-627)334375754 \|w (DE-600)2057709-6 \|w (DE-576)256145733 \|x 1568-4946 \|7 nnns
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publishDate	2017
allfields	10.1016/j.asoc.2017.09.002 doi (DE-627)ELV000847445 (ELSEVIER)S1568-4946(17)30541-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Mishra, Akash verfasserin aut Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes. Activation function adaptation Bi-modal derivative activation function Activation function Resilient backpropagation algorithm Sigmoidal feed-forward artificial neural network Chandra, Pravin verfasserin aut Ghose, Udayan verfasserin aut Sodhi, Sartaj Singh verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 61, Seite 983-994 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:61 pages:983-994 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 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_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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 61 983-994
spelling	10.1016/j.asoc.2017.09.002 doi (DE-627)ELV000847445 (ELSEVIER)S1568-4946(17)30541-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Mishra, Akash verfasserin aut Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes. Activation function adaptation Bi-modal derivative activation function Activation function Resilient backpropagation algorithm Sigmoidal feed-forward artificial neural network Chandra, Pravin verfasserin aut Ghose, Udayan verfasserin aut Sodhi, Sartaj Singh verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 61, Seite 983-994 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:61 pages:983-994 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 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_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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 61 983-994
allfields_unstemmed	10.1016/j.asoc.2017.09.002 doi (DE-627)ELV000847445 (ELSEVIER)S1568-4946(17)30541-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Mishra, Akash verfasserin aut Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes. Activation function adaptation Bi-modal derivative activation function Activation function Resilient backpropagation algorithm Sigmoidal feed-forward artificial neural network Chandra, Pravin verfasserin aut Ghose, Udayan verfasserin aut Sodhi, Sartaj Singh verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 61, Seite 983-994 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:61 pages:983-994 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 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_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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 61 983-994
allfieldsGer	10.1016/j.asoc.2017.09.002 doi (DE-627)ELV000847445 (ELSEVIER)S1568-4946(17)30541-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Mishra, Akash verfasserin aut Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes. Activation function adaptation Bi-modal derivative activation function Activation function Resilient backpropagation algorithm Sigmoidal feed-forward artificial neural network Chandra, Pravin verfasserin aut Ghose, Udayan verfasserin aut Sodhi, Sartaj Singh verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 61, Seite 983-994 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:61 pages:983-994 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 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_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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 61 983-994
allfieldsSound	10.1016/j.asoc.2017.09.002 doi (DE-627)ELV000847445 (ELSEVIER)S1568-4946(17)30541-0 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Mishra, Akash verfasserin aut Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes. Activation function adaptation Bi-modal derivative activation function Activation function Resilient backpropagation algorithm Sigmoidal feed-forward artificial neural network Chandra, Pravin verfasserin aut Ghose, Udayan verfasserin aut Sodhi, Sartaj Singh verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 61, Seite 983-994 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:61 pages:983-994 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 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_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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 61 983-994
language	English
source	Enthalten in Applied soft computing 61, Seite 983-994 volume:61 pages:983-994
sourceStr	Enthalten in Applied soft computing 61, Seite 983-994 volume:61 pages:983-994
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Activation function adaptation Bi-modal derivative activation function Activation function Resilient backpropagation algorithm Sigmoidal feed-forward artificial neural network
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied soft computing
authorswithroles_txt_mv	Mishra, Akash @@aut@@ Chandra, Pravin @@aut@@ Ghose, Udayan @@aut@@ Sodhi, Sartaj Singh @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	334375754
dewey-sort	14
id	ELV000847445
language_de	englisch
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author	Mishra, Akash
spellingShingle	Mishra, Akash ddc 004 bkl 54.00 misc Activation function adaptation misc Bi-modal derivative activation function misc Activation function misc Resilient backpropagation algorithm misc Sigmoidal feed-forward artificial neural network Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks
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topic_title	004 DE-600 54.00 bkl Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks Activation function adaptation Bi-modal derivative activation function Activation function Resilient backpropagation algorithm Sigmoidal feed-forward artificial neural network
topic	ddc 004 bkl 54.00 misc Activation function adaptation misc Bi-modal derivative activation function misc Activation function misc Resilient backpropagation algorithm misc Sigmoidal feed-forward artificial neural network
topic_unstemmed	ddc 004 bkl 54.00 misc Activation function adaptation misc Bi-modal derivative activation function misc Activation function misc Resilient backpropagation algorithm misc Sigmoidal feed-forward artificial neural network
topic_browse	ddc 004 bkl 54.00 misc Activation function adaptation misc Bi-modal derivative activation function misc Activation function misc Resilient backpropagation algorithm misc Sigmoidal feed-forward artificial neural network
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title	Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks
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title_full	Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks
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title_sort	bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks
title_auth	Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks
abstract	In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes.
abstractGer	In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes.
abstract_unstemmed	In this work an adaptive mechanism for choosing the activation function is proposed and described. Four bi-modal derivative sigmoidal adaptive activation function is used as the activation function at the hidden layer of a single hidden layer sigmoidal feedforward artificial neural networks. These four bi-modal derivative activation functions are grouped as asymmetric and anti-symmetric activation functions (in groups of two each). For the purpose of comparison, the logistic function (an asymmetric function) and the function obtained by subtracting 0.5 from it (an anti-symmetric) function is also used as activation function for the hidden layer nodes’. The resilient backpropagation algorithm with improved weight-tracking (iRprop+) is used to adapt the parameter of the activation functions and also the weights and/or biases of the sigmoidal feedforward artificial neural networks. The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. The obtained results demonstrate that both for asymmetric as well as anti-symmetric activation usage, the proposed/used adaptive activation functions are demonstratively as good as if not better than the sigmoidal function without any adaptive parameter when used as activation function of the hidden layer nodes.
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title_short	Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks
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author2	Chandra, Pravin Ghose, Udayan Sodhi, Sartaj Singh
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up_date	2024-07-06T19:23:02.912Z
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The learning tasks used to demonstrate the efficacy and efficiency of the proposed mechanism are 10 function approximation tasks and four real benchmark problems taken from the UCI machine learning repository. 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Pravin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghose, Udayan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sodhi, Sartaj Singh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied soft computing</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 2001</subfield><subfield code="g">61, Seite 983-994</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)334375754</subfield><subfield code="w">(DE-600)2057709-6</subfield><subfield code="w">(DE-576)256145733</subfield><subfield code="x">1568-4946</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" 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Bi-modal derivative adaptive activation function sigmoidal feedforward artificial neural networks

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