{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T21:02:27Z","timestamp":1763499747856,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00521-022-07581-y","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T20:02:50Z","timestamp":1658779370000},"page":"21035-21048","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Improved ZND model for solving dynamic linear complex matrix equation and its application"],"prefix":"10.1007","volume":"34","author":[{"given":"Zhiyuan","family":"Song","sequence":"first","affiliation":[]},{"given":"Zhenyao","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jiahao","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3389-6689","authenticated-orcid":false,"given":"Xiuchun","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Guancheng","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"issue":"1","key":"7581_CR1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/TCST.2019.2963017","volume":"29","author":"Z Xie","year":"2021","unstructured":"Xie Z, Jin L, Luo X, Li S, Xiao X (2021) A data-driven cyclic-motion generation scheme for kinematic control of redundant manipulators. IEEE Transact Control Syst Technol 29(1):53\u201363. https:\/\/doi.org\/10.1109\/TCST.2019.2963017","journal-title":"IEEE Transact Control Syst Technol"},{"issue":"9","key":"7581_CR2","doi-asserted-by":"publisher","first-page":"5172","DOI":"10.1109\/TII.2019.2899909","volume":"15","author":"Z Xie","year":"2019","unstructured":"Xie Z, Jin L, Du X, Xiao X, Li H, Li S (2019) On generalized rmp scheme for redundant robot manipulators aided with dynamic neural networks and nonconvex bound constraints. IEEE Transact Ind Inform 15(9):5172\u20135181. https:\/\/doi.org\/10.1109\/TII.2019.2899909","journal-title":"IEEE Transact Ind Inform"},{"key":"7581_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3052896","author":"VN Katsikis","year":"2021","unstructured":"Katsikis VN, Mourtas SD, Stanimirovi\u0107 PS, Zhang Y (2021) Solving complex-valued time-varying linear matrix equations via qr decomposition with applications to robotic motion tracking and on angle-of-arrival localization. IEEE Transact Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3052896","journal-title":"IEEE Transact Neural Netw Learn Syst"},{"key":"7581_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2937686","author":"X Xiao","year":"2019","unstructured":"Xiao X, Wei L, Fu D, Yan J, Wang H (2019) Noise-suppressing newton algorithm for kinematic control of robots. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2019.2937686","journal-title":"IEEE Access"},{"issue":"4","key":"7581_CR5","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.1109\/TII.2020.3005937","volume":"17","author":"H Huang","year":"2021","unstructured":"Huang H, Fu D, Xiao X, Ning Y, Wang H, Jin L, Liao S (2021) Modified newton integration neural algorithm for dynamic complex-valued matrix pseudoinversion applied to mobile object localization. IEEE Transact Ind Inform 17(4):2432\u20132442. https:\/\/doi.org\/10.1109\/TII.2020.3005937","journal-title":"IEEE Transact Ind Inform"},{"key":"7581_CR6","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.ins.2020.10.032","volume":"550","author":"G Wang","year":"2021","unstructured":"Wang G, Huang H, Shi L, Wang C, Fu D, Jin L, Xiuchun X (2021) A noise-suppressing newton-raphson iteration algorithm for solving the time-varying lyapunov equation and robotic tracking problems. Inform Sci 550:239\u2013251. https:\/\/doi.org\/10.1016\/j.ins.2020.10.032","journal-title":"Inform Sci"},{"issue":"10","key":"7581_CR7","doi-asserted-by":"publisher","first-page":"6359","DOI":"10.1109\/TII.2020.2964817","volume":"16","author":"L Jin","year":"2020","unstructured":"Jin L, Yan J, Du X, Xiao X, Fu D (2020) Rnn for solving time-variant generalized sylvester equation with applications to robots and acoustic source localization. IEEE Transact Ind Inform 16(10):6359\u20136369. https:\/\/doi.org\/10.1109\/TII.2020.2964817","journal-title":"IEEE Transact Ind Inform"},{"issue":"6","key":"7581_CR8","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1109\/TNN.2005.857946","volume":"16","author":"Y Zhang","year":"2005","unstructured":"Zhang Y, Ge SS (2005) Design and analysis of a general recurrent neural network model for time-varying matrix inversion. IEEE Transact Neural Netw 16(6):1477\u20131490. https:\/\/doi.org\/10.1109\/TNN.2005.857946","journal-title":"IEEE Transact Neural Netw"},{"issue":"4","key":"7581_CR9","doi-asserted-by":"publisher","first-page":"2167","DOI":"10.1109\/TII.2018.2865515","volume":"15","author":"F Xu","year":"2019","unstructured":"Xu F, Li Z, Nie Z, Shao H, Guo D (2019) New recurrent neural network for online solution of time-dependent underdetermined linear system with bound constraint. IEEE Transact Ind Inform 15(4):2167\u20132176. https:\/\/doi.org\/10.1109\/TII.2018.2865515","journal-title":"IEEE Transact Ind Inform"},{"issue":"8","key":"7581_CR10","doi-asserted-by":"publisher","first-page":"1940","DOI":"10.1109\/TAC.2009.2023779","volume":"54","author":"Y Zhang","year":"2009","unstructured":"Zhang Y, Chen K, Tan H-Z (2009) Performance analysis of gradient neural network exploited for online time-varying matrix inversion. IEEE Transaction Automatic Control 54(8):1940\u20131945. https:\/\/doi.org\/10.1109\/TAC.2009.2023779","journal-title":"IEEE Transaction Automatic Control"},{"key":"7581_CR11","doi-asserted-by":"crossref","unstructured":"Zhang Y, Chen K (2008) Comparison on zhang neural network and gradient neural network for time-varying linear matrix equation axb = c solving. In: 2008 IEEE International Conference on Industrial Technology, pp. 1\u20136.","DOI":"10.1109\/IITA.2008.73"},{"key":"7581_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2019.10.080","volume":"379","author":"S Liao","year":"2020","unstructured":"Liao S, Liu J, Xiao X, Fu D, Wang G, Jin L (2020) Modified gradient neural networks for solving the time-varying sylvester equation with adaptive coefficients and elimination of matrix inversion. Neurocomputing 379:1\u201311. https:\/\/doi.org\/10.1016\/j.neucom.2019.10.080","journal-title":"Neurocomputing"},{"issue":"5","key":"7581_CR13","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 Transact Neural Netw 13(5):1053\u20131063. https:\/\/doi.org\/10.1109\/TNN.2002.1031938","journal-title":"IEEE Transact Neural Netw"},{"issue":"4","key":"7581_CR14","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/MCI.2012.2215139","volume":"7","author":"D Guo","year":"2012","unstructured":"Guo D, Zhang Y (2012) Novel recurrent neural network for time-varying problems solving [research frontier]. IEEE Comput Intell Magazine 7(4):61\u201365. https:\/\/doi.org\/10.1109\/MCI.2012.2215139","journal-title":"IEEE Comput Intell Magazine"},{"key":"7581_CR15","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.amc.2015.02.060","volume":"259","author":"D Guo","year":"2015","unstructured":"Guo D, Zhang Y (2015) Znn for solving online time-varying linear matrix-vector inequality via equality conversion. Appl Math Comput 259:327\u2013338. https:\/\/doi.org\/10.1016\/j.amc.2015.02.060","journal-title":"Appl Math Comput"},{"key":"7581_CR16","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neucom.2013.04.041","volume":"121","author":"L Xiao","year":"2013","unstructured":"Xiao L, Zhang Y (2013) Different zhang functions resulting in different znn models demonstrated via time-varying linear matrix-vector inequalities solving. Neurocomputing 121:140\u2013149. https:\/\/doi.org\/10.1016\/j.neucom.2013.04.041","journal-title":"Neurocomputing"},{"issue":"2","key":"7581_CR17","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1109\/TNNLS.2013.2275011","volume":"25","author":"D Guo","year":"2014","unstructured":"Guo D, Zhang Y (2014) Zhang neural network for online solution of time-varying linear matrix inequality aided with an equality conversion. IEEE Transact Neural Netw Learn Syst 25(2):370\u2013382. https:\/\/doi.org\/10.1109\/TNNLS.2013.2275011","journal-title":"IEEE Transact Neural Netw Learn Syst"},{"issue":"2","key":"7581_CR18","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/S0096-3003(97)10064-9","volume":"93","author":"J Song","year":"1998","unstructured":"Song J, Yam Y (1998) Complex recurrent neural network for computing the inverse and pseudo-inverse of the complex matrix. Appl Math Comput 93(2):195\u2013205. https:\/\/doi.org\/10.1016\/S0096-3003(97)10064-9","journal-title":"Appl Math Comput"},{"issue":"24","key":"7581_CR19","doi-asserted-by":"publisher","first-page":"10066","DOI":"10.1016\/j.amc.2011.04.085","volume":"217","author":"Y Zhang","year":"2011","unstructured":"Zhang Y, Li Z, Li K (2011) Complex-valued zhang neural network for online complex-valued time-varying matrix inversion. Appl Math Comput 217(24):10066\u201310073. https:\/\/doi.org\/10.1016\/j.amc.2011.04.085","journal-title":"Appl Math Comput"},{"issue":"12","key":"7581_CR20","doi-asserted-by":"publisher","first-page":"4110","DOI":"10.1109\/TAC.2018.2810039","volume":"63","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Lu Y, Zheng L, Li S, Yu Z, Li Y (2018) A new varying-parameter convergent-differential neural-network for solving time-varying convex qp problem constrained by linear-equality. IEEE Transact Auto Cont 63(12):4110\u20134125. https:\/\/doi.org\/10.1109\/TAC.2018.2810039","journal-title":"IEEE Transact Auto Cont"},{"key":"7581_CR21","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.neucom.2015.04.070","volume":"167","author":"L Xiao","year":"2015","unstructured":"Xiao L (2015) A finite-time convergent neural dynamics for online solution of time-varying linear complex matrix equation. Neurocomputing 167:254\u2013259. https:\/\/doi.org\/10.1016\/j.neucom.2015.04.070","journal-title":"Neurocomputing"},{"issue":"10","key":"7581_CR22","doi-asserted-by":"publisher","first-page":"6634","DOI":"10.1109\/TII.2021.3049413","volume":"17","author":"L Xiao","year":"2021","unstructured":"Xiao L, Tao J, Dai J, Wang Y, Jia L, He Y (2021) A parameter-changing and complex-valued zeroing neural-network for finding solution of time-varying complex linear matrix equations in finite time. IEEE Transact Ind Inform 17(10):6634\u20136643. https:\/\/doi.org\/10.1109\/TII.2021.3049413","journal-title":"IEEE Transact Ind Inform"},{"issue":"6","key":"7581_CR23","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1109\/TFUZZ.2020.2981001","volume":"29","author":"L Jia","year":"2021","unstructured":"Jia L, Xiao L, Dai J, Qi Z, Zhang Z, Zhang Y (2021) Design and application of an adaptive fuzzy control strategy to zeroing neural network for solving time-variant qp problem. IEEE Transact Fuzzy Syst 29(6):1544\u20131555. https:\/\/doi.org\/10.1109\/TFUZZ.2020.2981001","journal-title":"IEEE Transact Fuzzy Syst"},{"key":"7581_CR24","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.ins.2020.03.043","volume":"524","author":"X Xiao","year":"2020","unstructured":"Xiao X, Jiang C, Lu H, Jin L, Liu D, Huang H, Pan Y (2020) A parallel computing method based on zeroing neural networks for time-varying complex-valued matrix moore-penrose inversion. Inform Sci 524:216\u2013228. https:\/\/doi.org\/10.1016\/j.ins.2020.03.043","journal-title":"Inform Sci"},{"issue":"7","key":"7581_CR25","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, Jia L, Tan H (2021) Finite-time and predefined-time convergence design for zeroing neural network: Theorem, method, and verification. IEEE Transact on Ind Inform 17(7):4724\u20134732. https:\/\/doi.org\/10.1109\/TII.2020.3021438","journal-title":"IEEE Transact on Ind Inform"},{"issue":"10","key":"7581_CR26","doi-asserted-by":"publisher","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 Transact Ind Inform 17(10):6864\u20136874. https:\/\/doi.org\/10.1109\/TII.2020.3047959","journal-title":"IEEE Transact Ind Inform"},{"key":"7581_CR27","doi-asserted-by":"publisher","first-page":"34492","DOI":"10.1109\/ACCESS.2020.2974753","volume":"8","author":"G Wang","year":"2020","unstructured":"Wang G, Huang H, Yan J, Cheng Y, Fu D (2020) An integration-implemented newton-raphson iterated algorithm with noise suppression for finding the solution of dynamic sylvester equation. IEEE Access 8:34492\u201334499. https:\/\/doi.org\/10.1109\/ACCESS.2020.2974753","journal-title":"IEEE Access"},{"key":"7581_CR28","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.ins.2021.12.039","volume":"588","author":"G Wang","year":"2022","unstructured":"Wang G, Hao Z, Zhang B, Jin L (2022) Convergence and robustness of bounded recurrent neural networks for solving dynamic lyapunov equations. Inform Sci 588:106\u2013123. https:\/\/doi.org\/10.1016\/j.ins.2021.12.039","journal-title":"Inform Sci"},{"key":"7581_CR29","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.neucom.2018.10.031","journal-title":"Neurocomputing"},{"issue":"10","key":"7581_CR30","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1007\/s00521-020-05356-x","journal-title":"Neural Comput Appl"},{"key":"7581_CR31","doi-asserted-by":"publisher","first-page":"41517","DOI":"10.1109\/ACCESS.2019.2907746","volume":"7","author":"J Yan","year":"2019","unstructured":"Yan J, Xiao X, Li H, Zhang J, Yan J, Liu M (2019) Noise-tolerant zeroing neural network for solving non-stationary lyapunov equation. IEEE Access 7:41517\u201341524. https:\/\/doi.org\/10.1109\/ACCESS.2019.2907746","journal-title":"IEEE Access"},{"key":"7581_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105384","author":"D Chen","year":"2011","unstructured":"Chen D, Li X, Li S (2011) A novel convolutional neural network model based on beetle antennae search optimization algorithm for computerized tomography diagnosis. IEEE Transact Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3105384","journal-title":"IEEE Transact Neural Netw Learn Syst"},{"issue":"4","key":"7581_CR33","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.1109\/TNNLS.2020.2991088","volume":"32","author":"D Chen","year":"2020","unstructured":"Chen D, Li S, Wu Q (2020) A novel supertwisting zeroing neural network with application to mobile robot manipulators. IEEE transact neural netw learn syst 32(4):1776\u20131787. https:\/\/doi.org\/10.1109\/TNNLS.2020.2991088","journal-title":"IEEE transact neural netw learn syst"},{"issue":"6","key":"7581_CR34","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1109\/TCYB.2019.2930662","volume":"50","author":"D Chen","year":"2020","unstructured":"Chen D, Li S, Lin F-J, Wu Q (2020) New super-twisting zeroing neural-dynamics model for tracking control of parallel robots: A finite-time and robust solution. IEEE Transact Cybern 50(6):2651\u20132660. https:\/\/doi.org\/10.1109\/TCYB.2019.2930662","journal-title":"IEEE Transact Cybern"},{"key":"7581_CR35","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.neucom.2020.02.011","volume":"394","author":"X Xiao","year":"2020","unstructured":"Xiao X, Fu D, Wang G, Liao S, Qi Y, Huang H, Jin L (2020) Two neural dynamics approaches for computing system of time-varying nonlinear equations. Neurocomputing 394:84\u201394. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.011","journal-title":"Neurocomputing"},{"issue":"8","key":"7581_CR36","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/TCYB.2013.2285166","volume":"44","author":"S Li","year":"2014","unstructured":"Li S, Li Y (2014) Nonlinearly activated neural network for solving time-varying complex sylvester equation. IEEE Transact Cybern 44(8):1397\u20131407. https:\/\/doi.org\/10.1109\/TCYB.2013.2285166","journal-title":"IEEE Transact Cybern"},{"key":"7581_CR37","doi-asserted-by":"crossref","unstructured":"Zhang Y, Shi Y, Xiao L, Mu B (2012) Convergence and stability results of zhang neural network solving systems of time-varying nonlinear equations. In: 2012 8th International Conference on Natural Computation, pp. 143\u2013147 . 10.1109\/ICNC.2012.6234592","DOI":"10.1109\/ICNC.2012.6234592"},{"issue":"7","key":"7581_CR38","doi-asserted-by":"publisher","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 Transact Cybern 50(7):3195\u20133207. https:\/\/doi.org\/10.1109\/TCYB.2019.2906263","journal-title":"IEEE Transact Cybern"},{"issue":"21","key":"7581_CR39","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(21):14231\u201314245. https:\/\/doi.org\/10.1007\/s00521-021-06068-6","journal-title":"Neural Comput Appl"},{"issue":"3","key":"7581_CR40","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1109\/LWC.2017.2777995","volume":"7","author":"A Noroozi","year":"2018","unstructured":"Noroozi A, Oveis AH, Hosseini SM, Sebt MA (2018) Improved algebraic solution for source localization from tdoa and fdoa measurements. IEEE Wireless Commun Letters 7(3):352\u2013355. https:\/\/doi.org\/10.1109\/LWC.2017.2777995","journal-title":"IEEE Wireless Commun Letters"},{"issue":"4","key":"7581_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JPHOT.2018.2841831","volume":"10","author":"P Du","year":"2018","unstructured":"Du P, Zhang S, Chen C, Alphones A, Zhong W-D (2018) Demonstration of a low-complexity indoor visible light positioning system using an enhanced tdoa scheme. IEEE Photonics J 10(4):1\u201310. https:\/\/doi.org\/10.1109\/JPHOT.2018.2841831","journal-title":"IEEE Photonics J"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07581-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07581-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07581-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T23:32:15Z","timestamp":1667863935000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07581-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":41,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["7581"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07581-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,7,25]]},"assertion":[{"value":"17 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 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":"We wish to draw the attention of the Editor to the following facts, which may be considered as potential conflicts of interest and to significant financial contributions to this work. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the other of authors listed in the manuscript has been approved by all of us and that the second author prepared the revision information letter and addressed most of the comments. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office ). He is responsible for communicating with the other authors about progress, submissions of revision and final approval of proof. We confirm that we have provided a current email address, which is accessible by the corresponding author, which has been configured to accept email from the Neural Computing and Applications Editorial Office.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}