{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T06:16:50Z","timestamp":1764829010726,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T00:00:00Z","timestamp":1662249600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T00:00:00Z","timestamp":1662249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006254"],"award-info":[{"award-number":["62006254"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2019A1515012128"],"award-info":[{"award-number":["2019A1515012128"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Plan Project of Guangzhou","award":["202102080656"],"award-info":[{"award-number":["202102080656"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s00521-022-07757-6","type":"journal-article","created":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T18:02:32Z","timestamp":1662314552000},"page":"2795-2809","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel discrete-time neurodynamic algorithm for future constrained quadratic programming with wheeled mobile robot control"],"prefix":"10.1007","volume":"35","author":[{"given":"Binbin","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1434-1020","authenticated-orcid":false,"given":"Xiao-Dong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,4]]},"reference":[{"key":"7757_CR1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex optimization","author":"S Boyd","year":"2004","unstructured":"Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge"},{"key":"7757_CR2","volume-title":"Numerical optimization","author":"J Nocedal","year":"2006","unstructured":"Nocedal J, Wright SJ (2006) Numerical optimization, 2nd edn. Springer, New York","edition":"2"},{"key":"7757_CR3","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/8996.001.0001","volume-title":"Optimization for machine learning","author":"S Sra","year":"2011","unstructured":"Sra S, Nowozin S, Wright SJ (2011) Optimization for machine learning. MIT Press, Cambridge"},{"issue":"15","key":"7757_CR4","first-page":"6652","volume":"217","author":"T Antczak","year":"2011","unstructured":"Antczak T (2011) A new exact exponential penalty function method and nonconvex mathematical programming. Appl Math Comput 217(15):6652\u20136662","journal-title":"Appl Math Comput"},{"key":"7757_CR5","doi-asserted-by":"crossref","DOI":"10.1002\/9781119381440","volume-title":"Robot manipulator redundancy resolution","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Jin L (2017) Robot manipulator redundancy resolution. Wiley, Hoboken"},{"key":"7757_CR6","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-10-7037-2","volume-title":"Neural networks for cooperative control of multiple robot arms","author":"S Li","year":"2018","unstructured":"Li S, Zhang Y (2018) Neural networks for cooperative control of multiple robot arms. Springer, Singapore"},{"issue":"3","key":"7757_CR7","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/TCI.2019.2896790","volume":"5","author":"Y Liu","year":"2019","unstructured":"Liu Y, Canu S, Honeine P, Ruan S (2019) Mixed integer programming for sparse coding: application to image denoising. IEEE Trans Comput Imag 5(3):354\u2013365","journal-title":"IEEE Trans Comput Imag"},{"issue":"1","key":"7757_CR8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s11063-017-9640-4","volume":"47","author":"A Nazemi","year":"2018","unstructured":"Nazemi A (2018) A capable neural network framework for solving degenerate quadratic optimization problems with an application in image fusion. Neural Process Lett 47(1):167\u2013192","journal-title":"Neural Process Lett"},{"issue":"10","key":"7757_CR9","doi-asserted-by":"crossref","first-page":"3063","DOI":"10.1109\/TCYB.2016.2567449","volume":"47","author":"S Qin","year":"2017","unstructured":"Qin S, Yang X, Xue X, Song J (2017) A one-layer recurrent neural network for pseudoconvex optimization problems with equality and inequality constraints. IEEE Trans Cybern 47(10):3063\u20133074","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"7757_CR10","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/TNNLS.2016.2524619","volume":"28","author":"Z Yan","year":"2017","unstructured":"Yan Z, Fan J, Wang J (2017) A collective neurodynamic approach to constrained global optimization. IEEE Trans Neural Netw Learn Syst 28(5):1206\u20131215","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"11","key":"7757_CR11","doi-asserted-by":"crossref","first-page":"3946","DOI":"10.1109\/TCYB.2018.2855724","volume":"49","author":"N Liu","year":"2019","unstructured":"Liu N, Qin S (2019) A novel neurodynamic approach to constrained complex-variable pseudoconvex optimization. IEEE Trans Cybern 49(11):3946\u20133956","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"7757_CR12","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1109\/TNN.2008.2011266","volume":"20","author":"X Hu","year":"2009","unstructured":"Hu X, Zhang B (2009) A new recurrent neural network for solving convex quadratic programming problems with an application to the $$k$$-winners-take-all problem. IEEE Trans Neural Netw 20(4):654\u2013664","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"7757_CR13","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1007\/s12559-014-9249-0","volume":"6","author":"A Nazemi","year":"2014","unstructured":"Nazemi A, Nazemi M (2014) A gradient-based neural network method for solving strictly convex quadratic programming problems. Cognitive Comput 6(3):484\u2013495","journal-title":"Cognitive Comput"},{"key":"7757_CR14","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neucom.2016.05.032","volume":"214","author":"X Huang","year":"2016","unstructured":"Huang X, Lou X, Cui B (2016) A novel neural network for solving convex quadratic programming problems subject to equality and inequality constraints. Neurocomputing 214:23\u201331","journal-title":"Neurocomputing"},{"key":"7757_CR15","volume-title":"Zhang neural networks and neural-dynamic method","author":"Y Zhang","year":"2011","unstructured":"Zhang Y, Yi C (2011) Zhang neural networks and neural-dynamic method. Nova, New York"},{"issue":"9","key":"7757_CR16","doi-asserted-by":"crossref","first-page":"4151","DOI":"10.1007\/s00521-019-04622-x","volume":"32","author":"J Jin","year":"2020","unstructured":"Jin J, Zhao L, Li M, Yu F, Xi Z (2020) Improved zeroing neural networks for finite time solving nonlinear equations. Neural Comput Appl 32(9):4151\u20134160","journal-title":"Neural Comput Appl"},{"issue":"15","key":"7757_CR17","doi-asserted-by":"crossref","first-page":"11505","DOI":"10.1007\/s00521-019-04639-2","volume":"32","author":"Z Sun","year":"2020","unstructured":"Sun Z, Shi T, Wei L, Sun Y, Liu K, Jin L (2020) Noise-suppressing zeroing neural network for online solving time-varying nonlinear optimization problem: a control-based approach. Neural Comput Appl 32(15):11505\u201311520","journal-title":"Neural Comput Appl"},{"issue":"21","key":"7757_CR18","doi-asserted-by":"crossref","first-page":"14231","DOI":"10.1007\/s00521-021-06068-6","volume":"33","author":"Z Ma","year":"2021","unstructured":"Ma Z, Yu S, Han Y, Guo D (2021) Zeroing neural network for bound-constrained time-varying nonlinear equation solving and its application to mobile robot manipulators. Neural Comput Appl 33(21):14231\u201314245","journal-title":"Neural Comput Appl"},{"issue":"10","key":"7757_CR19","doi-asserted-by":"crossref","first-page":"5327","DOI":"10.1007\/s00521-020-05356-x","volume":"33","author":"B Liao","year":"2021","unstructured":"Liao B, Wang Y, Li W, Peng C, Xiang Q (2021) Prescribed-time convergent and noise-tolerant Z-type neural dynamics for calculating time-dependent quadratic programming. Neural Comput Appl 33(10):5327\u20135337","journal-title":"Neural Comput Appl"},{"key":"7757_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3106044","author":"M Liu","year":"2021","unstructured":"Liu M, Chen L, Du X, Jin L, Shang M (2021) Activated gradients for deep neural networks. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3106044","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7757_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2022.3144135","author":"L Jin","year":"2022","unstructured":"Jin L, Wei L, Li S (2022) Gradient-based differential neural-solution to time-dependent nonlinear optimization. IEEE Trans Autom Control. https:\/\/doi.org\/10.1109\/TAC.2022.3144135","journal-title":"IEEE Trans Autom Control"},{"issue":"2","key":"7757_CR22","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s00521-021-06465-x","volume":"34","author":"W Li","year":"2022","unstructured":"Li W, Han L, Xiao X, Liao B, Peng C (2022) A gradient-based neural network accelerated for vision-based control of an RCM-constrained surgical endoscope robot. Neural Comput Appl 34(2):1329\u20131343","journal-title":"Neural Comput Appl"},{"issue":"10","key":"7757_CR23","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.3390\/electronics11101636","volume":"11","author":"B Liao","year":"2022","unstructured":"Liao B, Han L, He Y, Cao X, Li J (2022) Prescribed-time convergent adaptive ZNN for time-varying matrix inversion under harmonic noise. Electron 11(10):1636\u20131654","journal-title":"Electron"},{"issue":"4","key":"7757_CR24","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1109\/TNNLS.2020.3042761","volume":"33","author":"L Xiao","year":"2022","unstructured":"Xiao L, He Y, Dai J, Liu X, Liao B, Tan H (2022) A variable-parameter noise-tolerant zeroing neural network for time-variant matrix inversion with guaranteed robustness. IEEE Trans Neural Netw Learn Syst 33(4):1535\u20131545","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"7","key":"7757_CR25","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.1109\/TCYB.2019.2906263","volume":"50","author":"W Li","year":"2020","unstructured":"Li W, Xiao L, Liao B (2020) A finite-time convergent and noise-rejection recurrent neural network and its discretization for dynamic nonlinear equations solving. IEEE Trans Cybern 50(7):3195\u20133207","journal-title":"IEEE Trans Cybern"},{"issue":"11","key":"7757_CR26","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1109\/TSMC.2018.2836968","volume":"49","author":"L Xiao","year":"2019","unstructured":"Xiao L, Zhang Z, Li S (2019) Solving time-varying system of nonlinear equations by finite-time recurrent neural networks with application to motion tracking of robot manipulators. IEEE Trans Syst Man Cybern Syst 49(11):2210\u20132220","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"1","key":"7757_CR27","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TII.2017.2717020","volume":"14","author":"L Xiao","year":"2018","unstructured":"Xiao L, Liao B, Li S, Zhang Z, Ding L, Jin L (2018) Design and analysis of FTZNN applied to real-time solution of nonstationary Lyapunov equation and tracking control of wheeled mobile manipulator. IEEE Trans Ind Informat 14(1):98\u2013105","journal-title":"IEEE Trans Ind Informat"},{"key":"7757_CR28","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.neucom.2018.10.031","volume":"325","author":"B Liao","year":"2019","unstructured":"Liao B, Xiang Q, Li S (2019) Bounded Z-type neurodynamics with limited-time convergence and noise tolerance for calculating time-dependent Lyapunov equation. Neurocomputing 325:234\u2013241","journal-title":"Neurocomputing"},{"issue":"11","key":"7757_CR29","doi-asserted-by":"crossref","first-page":"6978","DOI":"10.1109\/TIE.2016.2590379","volume":"63","author":"L Jin","year":"2016","unstructured":"Jin L, Zhang Y, Li S, Zhang Y (2016) Modified ZNN for time-varying quadratic programming with inherent tolerance to noises and its application to kinematic redundancy resolution of robot manipulators. IEEE Trans Ind Electron 63(11):6978\u20136988","journal-title":"IEEE Trans Ind Electron"},{"key":"7757_CR30","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.neucom.2018.10.078","volume":"330","author":"Q Ma","year":"2019","unstructured":"Ma Q, Qin S, Jin T (2019) Complex Zhang neural networks for complex-variable dynamic quadratic programming. Neurocomputing 330:56\u201369","journal-title":"Neurocomputing"},{"issue":"9","key":"7757_CR31","doi-asserted-by":"crossref","first-page":"5330","DOI":"10.1109\/TII.2019.2897803","volume":"15","author":"W Li","year":"2019","unstructured":"Li W, Su Z, Tan Z (2019) A variable-gain finite-time convergent recurrent neural network for time-variant quadratic programming with unknown noises endured. IEEE Trans Ind Informat 15(9):5330\u20135340","journal-title":"IEEE Trans Ind Informat"},{"issue":"11","key":"7757_CR32","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.1109\/TNNLS.2019.2891252","volume":"30","author":"L Xiao","year":"2019","unstructured":"Xiao L, Li K, Duan M (2019) Computing time-varying quadratic optimization with finite-time convergence and noise tolerance: a unified framework for zeroing neural network. IEEE Trans Neural Netw Learn Syst 30(11):3360\u20133369","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"10","key":"7757_CR33","doi-asserted-by":"crossref","first-page":"6864","DOI":"10.1109\/TII.2020.3047959","volume":"17","author":"C Jiang","year":"2021","unstructured":"Jiang C, Xiao X, Liu D, Huang H, Xiao H, Lu H (2021) Nonconvex and bound constraint zeroing neural network for solving time-varying complex-valued quadratic programming problem. IEEE Trans Ind Informat 17(10):6864\u20136874","journal-title":"IEEE Trans Ind Informat"},{"issue":"8","key":"7757_CR34","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1109\/TNNLS.2018.2885042","volume":"30","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Kong LD, Zheng L (2019) Power-type varying-parameter RNN for solving TVQP problems: design, analysis, and applications. IEEE Trans Neural Netw Learn Syst 30(8):2419\u20132433","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"7","key":"7757_CR35","doi-asserted-by":"crossref","first-page":"4028","DOI":"10.1109\/TSMC.2019.2930763","volume":"51","author":"W Li","year":"2021","unstructured":"Li W, Ma X, Luo J, Jin L (2021) A strictly predefined-time convergent neural solution to equality- and inequality-constrained time-variant quadratic programming. IEEE Trans Syst Man Cybern Syst 51(7):4028\u20134039","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"3","key":"7757_CR36","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1109\/TII.2020.2996215","volume":"17","author":"Z Hu","year":"2021","unstructured":"Hu Z, Xiao L, Dai J, Xu Y, Zuo Q, Liu C (2021) A unified predefined-time convergent and robust ZNN model for constrained quadratic programming. IEEE Trans Ind Informat 17(3):1998\u20132010","journal-title":"IEEE Trans Ind Informat"},{"issue":"7","key":"7757_CR37","doi-asserted-by":"crossref","first-page":"2993","DOI":"10.1109\/TNNLS.2020.3009201","volume":"32","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Yang S, Zheng L (2021) A penalty strategy combined varying-parameter recurrent neural network for solving time-varying multi-type constrained quadratic programming problems. IEEE Trans Neural Netw Learn Syst 32(7):2993\u20133004","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7757_CR38","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neucom.2017.05.017","volume":"267","author":"L Jin","year":"2017","unstructured":"Jin L, Li S (2017) Nonconvex function activated zeroing neural network models for dynamic quadratic programming subject to equality and inequality constraints. Neurocomputing 267:107\u2013113","journal-title":"Neurocomputing"},{"key":"7757_CR39","volume-title":"Numerical methods using MATLAB","author":"JH Mathews","year":"2004","unstructured":"Mathews JH, Fink KD (2004) Numerical methods using MATLAB. Prentice Hall, New Jersey"},{"issue":"20","key":"7757_CR40","doi-asserted-by":"crossref","first-page":"5481","DOI":"10.1109\/TSP.2017.2728498","volume":"65","author":"A Simonetto","year":"2017","unstructured":"Simonetto A, Dall\u2019Anese E (2017) Prediction-correction algorithms for time-varying constrained optimization. IEEE Trans Signal Process 65(20):5481\u20135494","journal-title":"IEEE Trans Signal Process"},{"issue":"3","key":"7757_CR41","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/TSMC.2020.3020145","volume":"52","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Deng X, He M, Chen T, Liang J (2022) Runge\u2013Kutta type discrete circadian RNN for resolving tri-criteria optimization scheme of noises perturbed redundant robot manipulators. IEEE Trans Syst Man Cybern Syst 52(3):1405\u20131416","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"2","key":"7757_CR42","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s11075-018-0561-8","volume":"81","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Qi Z, Li J, Qiu B, Yang M (2019) Stepsize domain confirmation and optimum of ZeaD formula for future optimization. Numer Algorithms 81(2):561\u2013574","journal-title":"Numer Algorithms"},{"issue":"3","key":"7757_CR43","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1109\/TNNLS.2018.2861404","volume":"30","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Gong H, Yang M, Li J, Yang X (2019) Stepsize range and optimal value for Taylor\u2013Zhang discretization formula applied to zeroing neurodynamics illustrated via future equality-constrained quadratic programming. IEEE Trans Neural Netw Learn Syst 30(3):959\u2013966","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"7757_CR44","first-page":"1662","volume":"51","author":"J Guo","year":"2021","unstructured":"Guo J, Zhang Y (2021) Stepsize interval confirmation of general four-step DTZN algorithm illustrated with future quadratic programming and tracking control of manipulators. IEEE Trans Syst Man Cybern Syst 51(3):1662\u20131670","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"9","key":"7757_CR45","doi-asserted-by":"crossref","first-page":"4248","DOI":"10.1109\/TNNLS.2017.2761443","volume":"29","author":"D Guo","year":"2018","unstructured":"Guo D, Yan L, Nie Z (2018) Design, analysis, and representation of novel five-step DTZD algorithm for time-varying nonlinear optimization. IEEE Trans Neural Netw Learn Syst 29(9):4248\u20134260","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"7757_CR46","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1109\/TNNLS.2020.2995396","volume":"32","author":"J Li","year":"2021","unstructured":"Li J, Shi Y, Xuan H (2021) Unified model solving nine types of time-varying problems in the frame of zeroing neural network. IEEE Trans Neural Netw Learn Syst 32(5):1896\u20131905","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"7757_CR47","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1109\/TIE.2020.2970669","volume":"68","author":"J Guo","year":"2021","unstructured":"Guo J, Qiu B, Zhang Y (2021) Future different-layer linear equation and bounded inequality solved by combining Adams-Bashforth methods with CZNN model. IEEE Trans Ind Electron 68(2):1515\u20131524","journal-title":"IEEE Trans Ind Electron"},{"issue":"8","key":"7757_CR48","doi-asserted-by":"crossref","first-page":"5164","DOI":"10.1109\/TII.2020.3032158","volume":"17","author":"B Qiu","year":"2021","unstructured":"Qiu B, Guo J, Li X, Zhang Y (2021) New discretized zeroing neural network models for solving future system of bounded inequalities and nonlinear equations aided with general explicit linear four-step rule. IEEE Trans Ind Informat 17(8):5164\u20135174","journal-title":"IEEE Trans Ind Informat"},{"issue":"11","key":"7757_CR49","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1007\/s00521-016-2640-x","volume":"29","author":"L Jin","year":"2018","unstructured":"Jin L, Zhang Y, Qiu B (2018) Neural network-based discrete-time Z-type model of high accuracy in noisy environments for solving dynamic system of linear equations. Neural Comput Appl 29(11):1217\u20131232","journal-title":"Neural Comput Appl"},{"key":"7757_CR50","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-85729-148-6","volume-title":"Numerical methods for ordinary differential equations: initial value problems","author":"DF Griffiths","year":"2010","unstructured":"Griffiths DF, Higham DJ (2010) Numerical methods for ordinary differential equations: initial value problems. Springer, London"},{"issue":"2","key":"7757_CR51","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1109\/TCYB.2013.2253461","volume":"44","author":"L Xiao","year":"2014","unstructured":"Xiao L, Zhang Y (2014) A new performance index for the repetitive motion of mobile manipulators. IEEE Trans Cybern 44(2):280\u2013292","journal-title":"IEEE Trans Cybern"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07757-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07757-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07757-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:18:56Z","timestamp":1673846336000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07757-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,4]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["7757"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07757-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,9,4]]},"assertion":[{"value":"30 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This paper does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}