{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T22:26:37Z","timestamp":1759616797120,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["2018AAA0100101"],"award-info":[{"award-number":["2018AAA0100101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932006"],"award-info":[{"award-number":["61932006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chongqing technology innovation and application development project","award":["cstc2020jscx-msxmX0156"],"award-info":[{"award-number":["cstc2020jscx-msxmX0156"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00521-022-08166-5","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T18:02:52Z","timestamp":1671732172000},"page":"9501-9515","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Neurodynamic approaches with derivative feedback for sparse signal reconstruction"],"prefix":"10.1007","volume":"35","author":[{"given":"Xian","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongying","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"issue":"8","key":"8166_CR1","doi-asserted-by":"publisher","first-page":"3010","DOI":"10.1109\/TSP.2005.850882","volume":"53","author":"D Malioutov","year":"2005","unstructured":"Malioutov D, Cetin M, Willsky AS (2005) A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans Signal Process 53(8):3010\u20133022. https:\/\/doi.org\/10.1109\/TSP.2005.850882","journal-title":"IEEE Trans Signal Process"},{"issue":"8","key":"8166_CR2","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1109\/TSP.2015.2408558","volume":"63","author":"J Tan","year":"2015","unstructured":"Tan J, Ma Y, Baron D (2015) Compressive imaging via approximate message passing with image denoising. IEEE Trans Signal Process 63(8):2085\u20132092. https:\/\/doi.org\/10.1109\/TSP.2015.2408558","journal-title":"IEEE Trans Signal Process"},{"key":"8166_CR3","doi-asserted-by":"publisher","first-page":"2705","DOI":"10.1007\/s00521-018-3812-7","volume":"32","author":"M Ragab","year":"2018","unstructured":"Ragab M, Omer OA, Abdel-Nasser M (2018) Compressive sensing MRI reconstruction using empirical wavelet transform and grey wolf optimizer. Neural Comput Appl 32:2705\u20132724. https:\/\/doi.org\/10.1007\/s00521-018-3812-7","journal-title":"Neural Comput Appl"},{"issue":"2","key":"8166_CR4","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210\u2013227. https:\/\/doi.org\/10.1109\/TPAMI.2008.79","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"8166_CR5","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"DL Donoho","year":"2006","unstructured":"Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289\u20131306. https:\/\/doi.org\/10.1109\/TIT.2006.871582","journal-title":"IEEE Trans Inf Theory"},{"issue":"12","key":"8166_CR6","doi-asserted-by":"publisher","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","volume":"53","author":"JA Tropp","year":"2007","unstructured":"Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655\u20134666. https:\/\/doi.org\/10.1109\/TIT.2007.909108","journal-title":"IEEE Trans Inf Theory"},{"key":"8166_CR7","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1137\/S1052623497325107","volume":"9","author":"RH Byrd","year":"1999","unstructured":"Byrd RH, Hribar ME, Nocedal J (1999) An interior point algorithm for large-scale nonlinear programming. SIAM J Optim 9:877\u2013900","journal-title":"SIAM J Optim"},{"key":"8166_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1137\/090756855","volume":"4","author":"S Becker","year":"2011","unstructured":"Becker S, Bobin J, Cand\u00e8s EJ (2011) Nesta: a fast and accurate first-order method for sparse recovery. SIAM J Imaging Sci 4:1\u201339","journal-title":"SIAM J Imaging Sci"},{"key":"8166_CR9","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/s00521-012-0918-1","volume":"23","author":"B Huang","year":"2012","unstructured":"Huang B, Zhang H, Gong D, Wang Z (2012) A new result for projection neural networks to solve linear variational inequalities and related optimization problems. Neural Comput Appl 23:357\u2013362. https:\/\/doi.org\/10.1007\/s00521-012-0918-1","journal-title":"Neural Comput Appl"},{"key":"8166_CR10","doi-asserted-by":"publisher","first-page":"3399","DOI":"10.1007\/s00521-017-2926-7","volume":"30","author":"J Feng","year":"2017","unstructured":"Feng J, Qin S, Shi F, Zhao X (2017) A recurrent neural network with finite-time convergence for convex quadratic bilevel programming problems. Neural Comput Appl 30:3399\u20133408. https:\/\/doi.org\/10.1007\/s00521-017-2926-7","journal-title":"Neural Comput Appl"},{"key":"8166_CR11","doi-asserted-by":"publisher","first-page":"2526","DOI":"10.1162\/neco.2008.03-07-486","volume":"20","author":"CJ Rozell","year":"2008","unstructured":"Rozell CJ, Johnson DH, Baraniuk R, Olshausen BA (2008) Sparse coding via thresholding and local competition in neural circuits. Neural Comput 20:2526\u20132563. https:\/\/doi.org\/10.1162\/neco.2008.03-07-486","journal-title":"Neural Comput"},{"key":"8166_CR12","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol 58:267\u2013288","journal-title":"J R Stat Soc Ser B Methodol"},{"issue":"10","key":"8166_CR13","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1109\/TNNLS.2016.2575860","volume":"28","author":"R Feng","year":"2017","unstructured":"Feng R, Leung C-S, Constantinides AG, Zeng W-J (2017) Lagrange programming neural network for nondifferentiable optimization problems in sparse approximation. IEEE Trans Neural Netw Learn Syst 28(10):2395\u20132407. https:\/\/doi.org\/10.1109\/TNNLS.2016.2575860","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"8166_CR14","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1109\/TNNLS.2015.2481006","volume":"27","author":"Q Liu","year":"2016","unstructured":"Liu Q, Wang J (2016) $$l_{1}$$-minimization algorithms for sparse signal reconstruction based on a projection neural network. IEEE Trans Neural Netw Learn Syst 27(3):698\u2013707. https:\/\/doi.org\/10.1109\/TNNLS.2015.2481006","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"8166_CR15","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TNNLS.2018.2836933","volume":"30","author":"B Xu","year":"2019","unstructured":"Xu B, Liu Q, Huang T (2019) A discrete-time projection neural network for sparse signal reconstruction with application to face recognition. IEEE Trans Neural Netw Learn Syst 30(1):151\u2013162. https:\/\/doi.org\/10.1109\/TNNLS.2018.2836933","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"8166_CR16","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1109\/TNNLS.2011.2181867","volume":"23","author":"W Bian","year":"2012","unstructured":"Bian W, Chen X (2012) Smoothing neural network for constrained non-lipschitz optimization with applications. IEEE Trans Neural Netw Learn Syst 23(3):399\u2013411. https:\/\/doi.org\/10.1109\/TNNLS.2011.2181867","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8166_CR17","doi-asserted-by":"publisher","first-page":"2905","DOI":"10.1007\/s00521-017-3239-6","volume":"31","author":"D Wang","year":"2017","unstructured":"Wang D, Zhang Z (2017) KKT condition-based smoothing recurrent neural network for nonsmooth nonconvex optimization in compressed sensing. Neural Comput Appl 31:2905\u20132920. https:\/\/doi.org\/10.1007\/s00521-017-3239-6","journal-title":"Neural Comput Appl"},{"key":"8166_CR18","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.neunet.2019.10.006","volume":"122","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, He X, Huang T, Huang J, Li P (2020) A smoothing neural network for minimization $$ l_{1}-l_{p} $$ in sparse signal reconstruction with measurement noises. Neural Netw 122:40\u201353. https:\/\/doi.org\/10.1016\/j.neunet.2019.10.006","journal-title":"Neural Netw"},{"key":"8166_CR19","doi-asserted-by":"publisher","first-page":"6175","DOI":"10.1007\/s00521-019-04116-w","volume":"32","author":"T Xie","year":"2019","unstructured":"Xie T, Chen G, Liao X (2019) Event-triggered asynchronous distributed optimization algorithm with heterogeneous time-varying step-sizes. Neural Comput Appl 32:6175\u20136184. https:\/\/doi.org\/10.1007\/s00521-019-04116-w","journal-title":"Neural Comput Appl"},{"issue":"12","key":"8166_CR20","doi-asserted-by":"publisher","first-page":"3310","DOI":"10.1109\/TAC.2015.2416927","volume":"60","author":"Q Liu","year":"2015","unstructured":"Liu Q, Wang J (2015) A second-order multi-agent network for bound-constrained distributed optimization. IEEE Trans Autom Control 60(12):3310\u20133315. https:\/\/doi.org\/10.1109\/TAC.2015.2416927","journal-title":"IEEE Trans Autom Control"},{"issue":"7","key":"8166_CR21","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.1109\/TAC.2016.2610945","volume":"62","author":"S Yang","year":"2017","unstructured":"Yang S, Liu Q, Wang J (2017) A multi-agent system with a proportional-integral protocol for distributed constrained optimization. IEEE Trans Autom Control 62(7):3461\u20133467. https:\/\/doi.org\/10.1109\/TAC.2016.2610945","journal-title":"IEEE Trans Autom Control"},{"issue":"10","key":"8166_CR22","doi-asserted-by":"publisher","first-page":"5227","DOI":"10.1109\/TAC.2016.2628807","volume":"62","author":"X Zeng","year":"2017","unstructured":"Zeng X, Yi P, Hong Y (2017) Distributed continuous-time algorithm for constrained convex optimizations via nonsmooth analysis approach. IEEE Trans Autom Control 62(10):5227\u20135233. https:\/\/doi.org\/10.1109\/TAC.2016.2628807","journal-title":"IEEE Trans Autom Control"},{"issue":"12","key":"8166_CR23","doi-asserted-by":"publisher","first-page":"2700","DOI":"10.1109\/TSMC.2017.2780194","volume":"49","author":"X He","year":"2019","unstructured":"He X, Huang T, Yu J, Li C, Zhang Y (2019) A continuous-time algorithm for distributed optimization based on multiagent networks. IEEE Trans Syst Man Cybern Syst 49(12):2700\u20132709. https:\/\/doi.org\/10.1109\/TSMC.2017.2780194","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"8166_CR24","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neunet.2019.02.002","volume":"114","author":"H Che","year":"2019","unstructured":"Che H, Wang J (2019) A collaborative neurodynamic approach to global and combinatorial optimization. Neural Netw 114:15\u201327. https:\/\/doi.org\/10.1016\/j.neunet.2019.02.002","journal-title":"Neural Netw"},{"issue":"5","key":"8166_CR25","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1109\/TII.2015.2479558","volume":"12","author":"C Li","year":"2016","unstructured":"Li C, Yu X, Yu W, Huang T, Liu Z-W (2016) Distributed event-triggered scheme for economic dispatch in smart grids. IEEE Trans Ind Inf 12(5):1775\u20131785. https:\/\/doi.org\/10.1109\/TII.2015.2479558","journal-title":"IEEE Trans Ind Inf"},{"issue":"6","key":"8166_CR26","doi-asserted-by":"publisher","first-page":"2407","DOI":"10.1109\/TNNLS.2017.2691760","volume":"29","author":"C Li","year":"2018","unstructured":"Li C, Yu X, Huang T, He X (2018) Distributed optimal consensus over resource allocation network and its application to dynamical economic dispatch. IEEE Trans Neural Netw Learn Syst 29(6):2407\u20132418. https:\/\/doi.org\/10.1109\/TNNLS.2017.2691760","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"doi-asserted-by":"publisher","unstructured":"Qin S, Zhang YD, Wu Q, Amin MG (2014) Large-scale sparse reconstruction through partitioned compressive sensing. In: 2014 19th international conference on digital signal processing, pp 837\u2013840. https:\/\/doi.org\/10.1109\/ICDSP.2014.6900784","key":"8166_CR27","DOI":"10.1109\/ICDSP.2014.6900784"},{"key":"8166_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3085314","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Liao X, He X, Tang R (2021) Centralized and collective neurodynamic optimization approaches for sparse signal reconstruction via $$ l_{1} $$-minimization. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3085314","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"8166_CR29","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107282094","volume-title":"A gentle introduction to optimization","author":"B Guenin","year":"2014","unstructured":"Guenin B, K\u00f6nemann J, Tun\u00e7el L (2014) A gentle introduction to optimization. Cambridge University Press, Cambridge. https:\/\/doi.org\/10.1017\/CBO9781107282094"},{"key":"8166_CR30","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/j.crma.2008.03.014","volume":"346","author":"EJ Cand\u00e8s","year":"2008","unstructured":"Cand\u00e8s EJ (2008) The restricted isometry property and its implications for compressed sensing. C R Math 346:589\u2013592","journal-title":"C R Math"},{"key":"8166_CR31","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1561\/2400000003","volume":"1","author":"N Parikh","year":"2014","unstructured":"Parikh N, Boyd SP (2014) Proximal algorithms. Found Trends Optim 1:127\u2013239","journal-title":"Found Trends Optim"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08166-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08166-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08166-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T22:22:37Z","timestamp":1728598957000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08166-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,22]]},"references-count":31,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["8166"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08166-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,12,22]]},"assertion":[{"value":"11 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 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":"There are no conflicts of interest declared by the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}