{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T22:09:30Z","timestamp":1768514970309,"version":"3.49.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"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":["62073291"],"award-info":[{"award-number":["62073291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s00521-022-07820-2","type":"journal-article","created":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T12:02:16Z","timestamp":1664712136000},"page":"1675-1693","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Semi-global fixed\/predefined-time RNN models with comprehensive comparisons for time-variant neural computing"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2553-6154","authenticated-orcid":false,"given":"Mingxuan","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guomin","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"7820_CR1","volume-title":"Principles of neurocomputing for science and engineering","author":"FM Ham","year":"2001","unstructured":"Ham FM, Kostanic I (2001) Principles of neurocomputing for science and engineering. McGraw-Hill, New York"},{"key":"7820_CR2","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 Science Publishers, New York"},{"issue":"10","key":"7820_CR3","doi-asserted-by":"publisher","first-page":"3088","DOI":"10.1073\/pnas.81.10.3088","volume":"81","author":"JJ Hopfield","year":"1984","unstructured":"Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci 81(10):3088\u20133092","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"7820_CR4","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/81.167018","volume":"39","author":"A Cichocki","year":"1992","unstructured":"Cichocki A, Unbehauen R (1992) Neural networks for solving systems of linear equation and related problems. IEEE Trans Circuits Syst 39(2):124\u2013138","journal-title":"IEEE Trans Circuits Syst"},{"issue":"9","key":"7820_CR5","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/0898-1221(93)90003-E","volume":"26","author":"J Wang","year":"1993","unstructured":"Wang J (1993) Recurrent neural networks for solving linear matrix equations. Appl Math Comput 26(9):23\u201334","journal-title":"Appl Math Comput"},{"issue":"4","key":"7820_CR6","first-page":"452","volume":"62","author":"Y Xia","year":"1999","unstructured":"Xia Y, Wang J, Hung DL (1999) Recurrent neural networks for solving linear inequalities and equations. IEEE Trans Circuits Syst I Fund Theory Appl 62(4):452\u2013462","journal-title":"IEEE Trans Circuits Syst I Fund Theory Appl"},{"issue":"5","key":"7820_CR7","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1109\/31.1783","volume":"35","author":"M Kennedy","year":"1988","unstructured":"Kennedy M, Chua L (1988) Neural networks for nonlinear programming. IEEE Trans Circuits Syst 35(5):554\u2013562","journal-title":"IEEE Trans Circuits Syst"},{"issue":"4","key":"7820_CR8","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1109\/72.143372","volume":"3","author":"CY Maa","year":"1992","unstructured":"Maa CY, Shanblatt MA (1992) Linear and quadratic programming neural network analysis. IEEE Trans Neural Networks 3(4):580\u2013594","journal-title":"IEEE Trans Neural Networks"},{"issue":"9","key":"7820_CR9","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/81.244913","volume":"40","author":"J Wang","year":"1993","unstructured":"Wang J (1993) Analysis and design of a recurrent neural network for linear programming. IEEE Trans Circuits Syst I Fundam Theory Appl 40(9):613\u2013618","journal-title":"IEEE Trans Circuits Syst I Fundam Theory Appl"},{"issue":"4","key":"7820_CR10","doi-asserted-by":"publisher","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 Networks 20(4):654\u2013664","journal-title":"IEEE Trans Neural Networks"},{"issue":"11","key":"7820_CR11","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.1109\/TNNLS.2015.2425301","volume":"26","author":"Q Liu","year":"2015","unstructured":"Liu Q, Wang J (2015) A projection neural network for constrained quadratic minimax optimization. IEEE Trans Neural Networks Learn Syst 26(11):2891\u20132900","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"3","key":"7820_CR12","doi-asserted-by":"publisher","first-page":"6039","DOI":"10.1109\/LRA.2021.3089999","volume":"6","author":"W Qi","year":"2021","unstructured":"Qi W, Ovur SE, Li Z, Marzullo A, Song R (2021) Multi-sensor guided hand gestures recognition for teleoperated robot using recurrent neural network. IEEE Robot Autom Lett 6(3):6039\u20136045","journal-title":"IEEE Robot Autom Lett"},{"issue":"2","key":"7820_CR13","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TMMS.1969.299896","volume":"10","author":"DE Whitney","year":"1969","unstructured":"Whitney DE (1969) Resolved motion rate control of manipulators and human prostheses. IEEE Trans Man-Mach Syst 10(2):47\u201353","journal-title":"IEEE Trans Man-Mach Syst"},{"issue":"1","key":"7820_CR14","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/70.285587","volume":"10","author":"F Cheng","year":"1994","unstructured":"Cheng F, Chen T, Sun Y (1994) Resolving manipulator redundancy under inequality constraints. IEEE Trans Robot Autom 10(1):65\u201371","journal-title":"IEEE Trans Robot Autom"},{"key":"7820_CR15","doi-asserted-by":"crossref","unstructured":"Jin L, Wei L, Li S (2022) Gradient-based differential neural-solution to time-dependent nonlinear optimization. IEEE Trans Autom Control (In press)","DOI":"10.1109\/TAC.2022.3144135"},{"issue":"2","key":"7820_CR16","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1109\/70.88043","volume":"5","author":"CA Klein","year":"1989","unstructured":"Klein CA, Kee KB (1989) The nature of drift in pseudoinverse control of kinematically redundant manipulators. IEEE Trans Robot Autom 5(2):231\u2013234","journal-title":"IEEE Trans Robot Autom"},{"issue":"5","key":"7820_CR17","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1109\/TNN.2002.1031938","volume":"13","author":"Y Zhang","year":"2002","unstructured":"Zhang Y, Jiang D, Wang J (2002) A recurrent neural network for solving Sylvester equation with time-varying coefficients. IEEE Trans Neural Networks 13(5):1053\u20131063","journal-title":"IEEE Trans Neural Networks"},{"issue":"6","key":"7820_CR18","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1109\/TNN.2005.857946","volume":"16","author":"Y Zhang","year":"2005","unstructured":"Zhang Y, Ge S (2005) Design and analysis of a general recurrent neural network model for time-varying matrix inversion. IEEE Trans Neural Networks 16(6):1477\u20131490","journal-title":"IEEE Trans Neural Networks"},{"issue":"1","key":"7820_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-010-0452-y","volume":"20","author":"Y Zhang","year":"2011","unstructured":"Zhang Y, Yi C, Guo D, Zheng J (2011) Comparison on Zhang neural dynamics and gradient-based neural dynamics for online solution of nonlinear time-varying equation. Neural Comput Appl 20(1):1\u20137","journal-title":"Neural Comput Appl"},{"issue":"2","key":"7820_CR20","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TMECH.2011.2181977","volume":"18","author":"Z Zhang","year":"2013","unstructured":"Zhang Z, Zhang Y (2013) Variable joint-velocity limits of redundant robot manipulators handled by quadratic programming. IEEE\/ASME Trans Mech 18(2):674\u2013686","journal-title":"IEEE\/ASME Trans Mech"},{"key":"7820_CR21","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.neunet.2020.07.033","volume":"131","author":"H Su","year":"2020","unstructured":"Su H, Hu Y, Karimi HR et al (2020) Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results. Neural Networks 131:291\u2013299","journal-title":"Neural Networks"},{"key":"7820_CR22","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s11063-012-9241-1","volume":"37","author":"S Li","year":"2013","unstructured":"Li S, Chen S, Liu B (2013) Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by using a sign-bi-power activation function. Neural Process Lett 37:189\u2013205","journal-title":"Neural Process Lett"},{"key":"7820_CR23","doi-asserted-by":"publisher","first-page":"S793","DOI":"10.1007\/s00521-017-3010-z","volume":"31","author":"L Xiao","year":"2019","unstructured":"Xiao L (2019) A finite-time convergent Zhang neural network and its application to real-time matrix square root finding. Neural Comput Appl 31:S793\u2013S800","journal-title":"Neural Comput Appl"},{"key":"7820_CR24","doi-asserted-by":"publisher","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 32:4151\u20134160","journal-title":"Neural Comput Appl"},{"key":"7820_CR25","doi-asserted-by":"publisher","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 33:14231\u201314245","journal-title":"Neural Comput Appl"},{"issue":"8","key":"7820_CR26","doi-asserted-by":"publisher","first-page":"2980","DOI":"10.1109\/TNNLS.2019.2934734","volume":"31","author":"Z Tan","year":"2020","unstructured":"Tan Z, Li W, Xiao L, Hu Y (2020) New varying-parameter ZNN models with finite-time convergence and noise suppression for time-varying matrix Moore-Penrose inversion. IEEE Trans Neural Networks Learn Syst 31(8):2980\u20132992","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"12","key":"7820_CR27","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1109\/TNNLS.2015.2497715","volume":"27","author":"L Jin","year":"2016","unstructured":"Jin L, Zhang Y, Li S (2016) Integration-enhanced Zhang neural network for real-time-varying matrix inversion in the presence of various kinds of noises. IEEE Trans Neural Networks Learn Syst 27(12):2615\u20132627","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"11","key":"7820_CR28","doi-asserted-by":"publisher","first-page":"4362","DOI":"10.1109\/TSMC.2018.2853598","volume":"50","author":"W Li","year":"2020","unstructured":"Li W (2020) Design and analysis of a novel finite-time convergent and noise-tolerant recurrent neural network for time-variant matrix inversion. IEEE Trans Syst Man Cybern Syst 50(11):4362\u20134376","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"7820_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3051261","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Li Z, Yang S (2021) A barrier varying-parameter dynamic learning network for solving time-varying quadratic programming problems with multiple constraints. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2021.3051261","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"7820_CR30","doi-asserted-by":"publisher","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(2):11505\u201311520","journal-title":"Neural Comput Appl"},{"key":"7820_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084740","author":"M Sun","year":"2021","unstructured":"Sun M, Zhang Y, Wu Y, He X (2021) On a finitely-activated terminal RNN approach to time-variant problem solving. IEEE Trans Neural Networks Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3084740","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"8","key":"7820_CR32","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1109\/TAC.2011.2179869","volume":"57","author":"A Polyakov","year":"2012","unstructured":"Polyakov A (2012) Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Trans Autom Control 57(8):2106\u20132110","journal-title":"IEEE Trans Autom Control"},{"issue":"5","key":"7820_CR33","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TSMC.2017.2785314","volume":"50","author":"M Sun","year":"2020","unstructured":"Sun M (2020) Two-phase attractors for finite-duration consensus of multiagent systems. IEEE Trans Syst Man Cybern Syst 50(5):1757\u20131765","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"8","key":"7820_CR34","doi-asserted-by":"publisher","first-page":"5017","DOI":"10.1109\/TSMC.2019.2946328","volume":"51","author":"M Sun","year":"2021","unstructured":"Sun M, Li H, Li W (2021) On finite-duration convergent attracting laws. IEEE Trans Syst Man Cybern Syst 51(8):5017\u20135029","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"6","key":"7820_CR35","doi-asserted-by":"publisher","first-page":"2882","DOI":"10.1109\/TCYB.2020.2977934","volume":"51","author":"C Hu","year":"2021","unstructured":"Hu C, He H, Jiang H (2021) Fixed\/preassigned-time synchronization of complex networks via improving fixed-time stability. IEEE Trans Cybern 51(6):2882\u20132892","journal-title":"IEEE Trans Cybern"},{"key":"7820_CR36","first-page":"1","volume":"32","author":"JD Snchez-Torres","year":"2017","unstructured":"Snchez-Torres JD, Gmez-Gutirrez D, Lpez E, Loukianov AG (2017) A class of predefined-time stable dynamical systems. IMA J Math Control Inf 32:1\u201329","journal-title":"IMA J Math Control Inf"},{"issue":"12","key":"7820_CR37","doi-asserted-by":"publisher","first-page":"10732","DOI":"10.1109\/TIE.2019.2960754","volume":"67","author":"W Li","year":"2020","unstructured":"Li W (2020) Predefined-time convergent neural solution to cyclical motion planning of redundant robots under physical constraints. IEEE Trans Industr Electron 67(12):10732\u201310743","journal-title":"IEEE Trans Industr Electron"},{"issue":"9","key":"7820_CR38","doi-asserted-by":"publisher","first-page":"5161","DOI":"10.1109\/TII.2019.2900659","volume":"15","author":"L Xiao","year":"2019","unstructured":"Xiao L, Zhang Y, Hu Z et al (2019) Performance benefits of robust nonlinear zeroing neural network for finding accurate solution of Lyapunov equation in presence of various noises. IEEE Trans Industr Inf 15(9):5161\u20135171","journal-title":"IEEE Trans Industr Inf"},{"issue":"7","key":"7820_CR39","doi-asserted-by":"publisher","first-page":"4724","DOI":"10.1109\/TII.2020.3021438","volume":"17","author":"L Xiao","year":"2021","unstructured":"Xiao L, Cao Y, Dai J et al (2021) Finite-Time and predefined-time convergence design for zeroing neural network: theorem, method, and verification. IEEE Trans Industr Inf 17(7):4724\u20134732","journal-title":"IEEE Trans Industr Inf"},{"issue":"11","key":"7820_CR40","doi-asserted-by":"publisher","first-page":"7513","DOI":"10.1109\/TII.2021.3058343","volume":"17","author":"L Xiao","year":"2021","unstructured":"Xiao L, He Y (2021) A noise-suppression ZNN model with new variable parameter for dynamic Sylvester equation. IEEE Trans Industr Inf 17(11):7513\u20137522","journal-title":"IEEE Trans Industr Inf"},{"key":"7820_CR41","doi-asserted-by":"crossref","unstructured":"Jimenez-Rodriguez E, Sanchez-Torres JD, Loukianov AG (2017) Semi-global predefined-time stable systems. In: Proceedings of 14th International conference on electrical engineering, computing science and automatic control, Mexico City, Mexico, pp.1-6","DOI":"10.1109\/ICEEE.2017.8108875"},{"key":"7820_CR42","doi-asserted-by":"publisher","DOI":"10.1515\/9781400841042","volume-title":"Nonlinear dynamical systems and control: a lyapunov-based approach","author":"WM Haddad","year":"2008","unstructured":"Haddad WM, Vijaysekhar C (2008) Nonlinear dynamical systems and control: a lyapunov-based approach. Princeton University Press, Princeton NJ"},{"issue":"2","key":"7820_CR43","doi-asserted-by":"publisher","first-page":"151","DOI":"10.2307\/2332208","volume":"32","author":"C Thompson","year":"1941","unstructured":"Thompson C, Pearson E, Comrie L, Hartley H (1941) Tables of percentage points of the incomplete beta-function. Biometrika 32(2):151\u2013181","journal-title":"Biometrika"},{"key":"7820_CR44","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107325937","volume-title":"Special functions","author":"G Andrews","year":"1999","unstructured":"Andrews G, Askey R, Roy R (1999) Special functions. Cambridge Univ. Press, Cambridge"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07820-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07820-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07820-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T17:22:34Z","timestamp":1673284954000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07820-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,2]]},"references-count":44,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["7820"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07820-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,2]]},"assertion":[{"value":"5 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 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 interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}