A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators

Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditiona...
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Autor*in:	Shi, Yang [verfasserIn] Sheng, Wangrong [verfasserIn] Li, Shuai [verfasserIn] Li, Bin [verfasserIn] Sun, Xiaobing [verfasserIn] Gerontitis, Dimitrios K. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Discrete time-variant nonlinear optimization (DTVNO) Discrete-time recurrent neurodynamics (DTRN) Direct discrete technique Convergence Robot manipulators

Übergeordnetes Werk:	Enthalten in: Neural networks - Amsterdam : Elsevier, 1988, 164, Seite 428-438
Übergeordnetes Werk:	volume:164 ; pages:428-438

DOI / URN:	10.1016/j.neunet.2023.04.040

Katalog-ID:	ELV010429964

Internformat


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024	7		\|a 10.1016/j.neunet.2023.04.040 \|2 doi
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100	1		\|a Shi, Yang \|e verfasserin \|0 (orcid)0000-0003-3014-7858 \|4 aut
245	1	0	\|a A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators
264		1	\|c 2023
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337			\|a Computermedien \|b c \|2 rdamedia
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520			\|a Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method.
650		4	\|a Discrete time-variant nonlinear optimization (DTVNO)
650		4	\|a Discrete-time recurrent neurodynamics (DTRN)
650		4	\|a Direct discrete technique
650		4	\|a Convergence
650		4	\|a Robot manipulators
700	1		\|a Sheng, Wangrong \|e verfasserin \|4 aut
700	1		\|a Li, Shuai \|e verfasserin \|4 aut
700	1		\|a Li, Bin \|e verfasserin \|4 aut
700	1		\|a Sun, Xiaobing \|e verfasserin \|4 aut
700	1		\|a Gerontitis, Dimitrios K. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Neural networks \|d Amsterdam : Elsevier, 1988 \|g 164, Seite 428-438 \|h Online-Ressource \|w (DE-627)302468536 \|w (DE-600)1491372-0 \|w (DE-576)07971997X \|x 1879-2782 \|7 nnns
773	1	8	\|g volume:164 \|g pages:428-438
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912			\|a GBV_ILN_2014
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912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
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912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
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912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
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912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
952			\|d 164 \|h 428-438

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author_variant	y s ys w s ws s l sl b l bl x s xs d k g dk dkg
matchkey_str	article:18792782:2023----::drcdsrtztorcreterdnmcmtofrieainnnierpiiain
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publishDate	2023
allfields	10.1016/j.neunet.2023.04.040 doi (DE-627)ELV010429964 (ELSEVIER)S0893-6080(23)00226-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Shi, Yang verfasserin (orcid)0000-0003-3014-7858 aut A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method. Discrete time-variant nonlinear optimization (DTVNO) Discrete-time recurrent neurodynamics (DTRN) Direct discrete technique Convergence Robot manipulators Sheng, Wangrong verfasserin aut Li, Shuai verfasserin aut Li, Bin verfasserin aut Sun, Xiaobing verfasserin aut Gerontitis, Dimitrios K. verfasserin aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 164, Seite 428-438 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:164 pages:428-438 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 164 428-438
spelling	10.1016/j.neunet.2023.04.040 doi (DE-627)ELV010429964 (ELSEVIER)S0893-6080(23)00226-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Shi, Yang verfasserin (orcid)0000-0003-3014-7858 aut A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method. Discrete time-variant nonlinear optimization (DTVNO) Discrete-time recurrent neurodynamics (DTRN) Direct discrete technique Convergence Robot manipulators Sheng, Wangrong verfasserin aut Li, Shuai verfasserin aut Li, Bin verfasserin aut Sun, Xiaobing verfasserin aut Gerontitis, Dimitrios K. verfasserin aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 164, Seite 428-438 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:164 pages:428-438 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 164 428-438
allfields_unstemmed	10.1016/j.neunet.2023.04.040 doi (DE-627)ELV010429964 (ELSEVIER)S0893-6080(23)00226-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Shi, Yang verfasserin (orcid)0000-0003-3014-7858 aut A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method. Discrete time-variant nonlinear optimization (DTVNO) Discrete-time recurrent neurodynamics (DTRN) Direct discrete technique Convergence Robot manipulators Sheng, Wangrong verfasserin aut Li, Shuai verfasserin aut Li, Bin verfasserin aut Sun, Xiaobing verfasserin aut Gerontitis, Dimitrios K. verfasserin aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 164, Seite 428-438 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:164 pages:428-438 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 164 428-438
allfieldsGer	10.1016/j.neunet.2023.04.040 doi (DE-627)ELV010429964 (ELSEVIER)S0893-6080(23)00226-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Shi, Yang verfasserin (orcid)0000-0003-3014-7858 aut A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method. Discrete time-variant nonlinear optimization (DTVNO) Discrete-time recurrent neurodynamics (DTRN) Direct discrete technique Convergence Robot manipulators Sheng, Wangrong verfasserin aut Li, Shuai verfasserin aut Li, Bin verfasserin aut Sun, Xiaobing verfasserin aut Gerontitis, Dimitrios K. verfasserin aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 164, Seite 428-438 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:164 pages:428-438 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 164 428-438
allfieldsSound	10.1016/j.neunet.2023.04.040 doi (DE-627)ELV010429964 (ELSEVIER)S0893-6080(23)00226-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Shi, Yang verfasserin (orcid)0000-0003-3014-7858 aut A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method. Discrete time-variant nonlinear optimization (DTVNO) Discrete-time recurrent neurodynamics (DTRN) Direct discrete technique Convergence Robot manipulators Sheng, Wangrong verfasserin aut Li, Shuai verfasserin aut Li, Bin verfasserin aut Sun, Xiaobing verfasserin aut Gerontitis, Dimitrios K. verfasserin aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 164, Seite 428-438 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:164 pages:428-438 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 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_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 164 428-438
language	English
source	Enthalten in Neural networks 164, Seite 428-438 volume:164 pages:428-438
sourceStr	Enthalten in Neural networks 164, Seite 428-438 volume:164 pages:428-438
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Discrete time-variant nonlinear optimization (DTVNO) Discrete-time recurrent neurodynamics (DTRN) Direct discrete technique Convergence Robot manipulators
dewey-raw	004
isfreeaccess_bool	false
container_title	Neural networks
authorswithroles_txt_mv	Shi, Yang @@aut@@ Sheng, Wangrong @@aut@@ Li, Shuai @@aut@@ Li, Bin @@aut@@ Sun, Xiaobing @@aut@@ Gerontitis, Dimitrios K. @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	302468536
dewey-sort	14
id	ELV010429964
language_de	englisch
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author	Shi, Yang
spellingShingle	Shi, Yang ddc 004 bkl 54.72 misc Discrete time-variant nonlinear optimization (DTVNO) misc Discrete-time recurrent neurodynamics (DTRN) misc Direct discrete technique misc Convergence misc Robot manipulators A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators
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topic_title	004 VZ 54.72 bkl A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators Discrete time-variant nonlinear optimization (DTVNO) Discrete-time recurrent neurodynamics (DTRN) Direct discrete technique Convergence Robot manipulators
topic	ddc 004 bkl 54.72 misc Discrete time-variant nonlinear optimization (DTVNO) misc Discrete-time recurrent neurodynamics (DTRN) misc Direct discrete technique misc Convergence misc Robot manipulators
topic_unstemmed	ddc 004 bkl 54.72 misc Discrete time-variant nonlinear optimization (DTVNO) misc Discrete-time recurrent neurodynamics (DTRN) misc Direct discrete technique misc Convergence misc Robot manipulators
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title	A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators
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title_full	A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators
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author_browse	Shi, Yang Sheng, Wangrong Li, Shuai Li, Bin Sun, Xiaobing Gerontitis, Dimitrios K.
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title_sort	a direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators
title_auth	A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators
abstract	Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method.
abstractGer	Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method.
abstract_unstemmed	Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method.
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title_short	A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators
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author2	Sheng, Wangrong Li, Shuai Li, Bin Sun, Xiaobing Gerontitis, Dimitrios K.
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up_date	2024-07-06T17:57:45.961Z
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A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators

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