{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:26Z","timestamp":1740122786980,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T00:00:00Z","timestamp":1590451200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T00:00:00Z","timestamp":1590451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100010903","name":"Key Programme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010903","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2020,8]]},"DOI":"10.1007\/s11063-020-10262-3","type":"journal-article","created":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T17:02:45Z","timestamp":1590512565000},"page":"525-543","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Global Exponential Stability of Hybrid Non-autonomous Neural Networks with Markovian Switching"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3819-060X","authenticated-orcid":false,"given":"Chenhui","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghui","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,26]]},"reference":[{"issue":"1","key":"10262_CR1","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":"6","key":"10262_CR2","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/LSP.2019.2910403","volume":"26","author":"Z Liu","year":"2019","unstructured":"Liu Z, Xiao B, Alrabeiah M et al (2019) Single image dehazing with a generic model-agnostic convolutional neural network. IEEE Signal Process Lett 26(6):833\u2013837","journal-title":"IEEE Signal Process Lett"},{"issue":"1","key":"10262_CR3","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s11063-017-9709-0","volume":"48","author":"P Chang","year":"2018","unstructured":"Chang P, Zhang J, Hu J et al (2018) A deep neural network based on ELM for semi-supervised learning of image classification. Neural Process Lett 48(1):375\u2013388","journal-title":"Neural Process Lett"},{"issue":"2","key":"10262_CR4","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1007\/s11063-018-9830-8","volume":"48","author":"E Trentin","year":"2018","unstructured":"Trentin E, Schwenker F, Gayar NE et al (2018) Off the mainstream: advances in neural networks and machine learning for pattern recognition. Neural Process Lett 48(2):643\u2013648","journal-title":"Neural Process Lett"},{"key":"10262_CR5","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.neucom.2016.07.065","volume":"227","author":"J Yang","year":"2017","unstructured":"Yang J, Wang L, Wang Y et al (2017) A novel memristive Hopfield neural network with application in associative memory. Neurocomputing 227:142\u2013148","journal-title":"Neurocomputing"},{"issue":"5","key":"10262_CR6","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/TNNLS.2018.2870553","volume":"30","author":"B Hu","year":"2019","unstructured":"Hu B, Guan Z, Chen G et al (2019) Multistability of delayed hybrid impulsive neural networks with application to associative memories. IEEE Trans Neural Netw Learn Syst 30(5):1537\u20131551","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"10262_CR7","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1007\/s11063-016-9562-6","volume":"45","author":"A Nazemi","year":"2017","unstructured":"Nazemi A, Karami R (2017) A neural network approach for solving optimal control problems with inequality constraints and some applications. Neural Process Lett 45(3):995\u20131023","journal-title":"Neural Process Lett"},{"issue":"6","key":"10262_CR8","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TNNLS.2014.2334364","volume":"26","author":"S Qin","year":"2015","unstructured":"Qin S, Xue X (2015) A two-layer recurrent neural network for nonsmooth convex optimization problems. IEEE Trans Neural Netw Learn Syst 26(6):1149\u20131160","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"10262_CR9","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1109\/TNNLS.2013.2244099","volume":"24","author":"Z Uykan","year":"2013","unstructured":"Uykan Z (2013) Fast-convergent double-sigmoid Hopfield neural network as applied to optimization problems. IEEE Trans Neural Netw Learn Syst 24(6):990\u2013996","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"10262_CR10","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TNNLS.2015.2496658","volume":"27","author":"C Li","year":"2016","unstructured":"Li C, Yu X, Huang T et al (2016) A generalized Hopfield network for nonsmooth constrained convex optimization: lie derivative approach. IEEE Trans Neural Netw Learn Syst 27(2):308\u2013321","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"10262_CR11","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TNNLS.2012.2236352","volume":"24","author":"X Li","year":"2013","unstructured":"Li X, Song S (2013) Impulsive control for existence, uniqueness, and global stability of periodic solutions of recurrent neural networks with discrete and continuously distributed delays. IEEE Trans Neural Netw Learn Syst 24(6):868\u2013877","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"10262_CR12","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1007\/s11063-016-9563-5","volume":"45","author":"Z Wang","year":"2017","unstructured":"Wang Z, Guo Z, Huang L et al (2017) Dynamical behavior of complex-valued Hopfield neural networks with discontinuous activation functions. Neural Process Lett 45(3):1039\u20131061","journal-title":"Neural Process Lett"},{"key":"10262_CR13","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.amc.2018.11.063","volume":"348","author":"A Rathinasamy","year":"2019","unstructured":"Rathinasamy A, Narayanasamy J (2019) Mean square stability and almost sure exponential stability of two step Maruyama methods of stochastic delay Hopfield neural networks. Appl Math Comput 348:126\u2013152","journal-title":"Appl Math Comput"},{"issue":"1","key":"10262_CR14","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s11063-016-9571-5","volume":"46","author":"M Syed Ali","year":"2017","unstructured":"Syed Ali M, Yogambigai J (2017) Exponential stability of semi-Markovian switching complex dynamical networks with mixed time varying delays and impulse control. Neural Process Lett 46(1):113\u2013133","journal-title":"Neural Process Lett"},{"key":"10262_CR15","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1016\/j.neucom.2017.10.006","volume":"275","author":"L Liu","year":"2018","unstructured":"Liu L, Zhu Q, Feng L (2018) Lagrange stability for delayed recurrent neural networks with Markovian switching based on stochastic vector Halandy inequalities. Neurocomputing 275:1614\u20131621","journal-title":"Neurocomputing"},{"issue":"2","key":"10262_CR16","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s11063-013-9314-9","volume":"40","author":"D Li","year":"2014","unstructured":"Li D, Ma C (2014) Attractor and stochastic boundedness for stochastic infinite delay neural networks with Markovian switching. Neural Process Lett 40(2):127\u2013142","journal-title":"Neural Process Lett"},{"issue":"7","key":"10262_CR17","first-page":"3152","volume":"29","author":"L Liu","year":"2018","unstructured":"Liu L, Cao J, Qian C (2018) $$p$$th moment exponential input-to-State stability of delayed recurrent neural networks With Markovian switching via vector Lyapunov function. IEEE Trans Neural Netw Learn Syst 29(7):3152\u20133163","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"10262_CR18","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s11063-018-9912-7","volume":"50","author":"L Feng","year":"2019","unstructured":"Feng L, Cao J, Liu L (2019) Stability analysis in a class of Markov switched stochastic Hopfield neural networks. Neural Process Lett 50(1):413\u2013430","journal-title":"Neural Process Lett"},{"key":"10262_CR19","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.neucom.2018.10.003","volume":"323","author":"C Maharajan","year":"2019","unstructured":"Maharajan C, Raja R, Cao J et al (2019) Fractional delay segments method on time-delayed recurrent neural networks with impulsive and stochastic effects: an exponential stability approach. Neurocomputing 323:277\u2013298","journal-title":"Neurocomputing"},{"issue":"3","key":"10262_CR20","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1007\/s11071-017-3574-2","volume":"89","author":"Y Shu","year":"2017","unstructured":"Shu Y, Liu XG, Qiu S et al (2017) Dissipativity analysis for generalized neural networks with Markovian jump parameters and time-varying delay. Nonlinear Dyn 89(3):2125\u20132140","journal-title":"Nonlinear Dyn"},{"key":"10262_CR21","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.cnsns.2019.02.001","volume":"73","author":"P Wang","year":"2019","unstructured":"Wang P, Wang X, Su H (2019) Stability analysis for complex-valued stochastic delayed networks with Markovian switching and impulsive effects. Commun Nonlinear Sci Numer Simul 73:35\u201351","journal-title":"Commun Nonlinear Sci Numer Simul"},{"issue":"1","key":"10262_CR22","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s11063-018-9805-9","volume":"49","author":"D Xie","year":"2018","unstructured":"Xie D, Jiang Y, Han M (2018) Global exponential synchronization of complex-valued neural networks with time delays via matrix measure method. Neural Process Lett 49(1):187\u2013201","journal-title":"Neural Process Lett"},{"key":"10262_CR23","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.neunet.2019.05.024","volume":"117","author":"L Li","year":"2019","unstructured":"Li L, Shi X, Liang J (2019) Synchronization of impulsive coupled complex-valued neural networks with delay: the matrix measure method. Neural Netw 117:285\u2013294","journal-title":"Neural Netw"},{"issue":"2","key":"10262_CR24","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s00521-013-1507-7","volume":"25","author":"CD Zheng","year":"2014","unstructured":"Zheng CD, Zhang H, Wang Z (2014) Exponential synchronization of stochastic chaotic neural networks with mixed time delays and Markovian switching. Neural Comput Appl 25(2):429\u2013442","journal-title":"Neural Comput Appl"},{"issue":"6","key":"10262_CR25","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1109\/TNNLS.2017.2696582","volume":"29","author":"Y Wei","year":"2017","unstructured":"Wei Y, Park JH, Karimi HR et al (2017) Improved stability and stabilization results for stochastic synchronization of continuous-time semi-Markovian jump neural networks with time-varying delay. IEEE Trans Neural Netw Learn Syst 29(6):2488\u20132501","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"12","key":"10262_CR26","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1109\/TNNLS.2016.2609148","volume":"28","author":"R Li","year":"2016","unstructured":"Li R, Cao J (2016) Finite-time stability analysis for Markovian jump memristive neural networks with partly unknown transition probabilities. IEEE Trans Neural Netw Learn Syst 28(12):2924\u20132935","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"10262_CR27","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TNNLS.2016.2614998","volume":"29","author":"L Van Hien","year":"2018","unstructured":"Van Hien L, Son DT, Trinh H (2018) On global dissipativity of nonautonomous neural networks with multiple proportional delays. IEEE Trans Neural Netw Learn Syst 29(1):225\u2013231","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"10262_CR28","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1109\/TNNLS.2018.2874035","volume":"30","author":"H Shen","year":"2018","unstructured":"Shen H, Wang T, Cao J et al (2018) Nonfragile dissipative synchronization for Markovian memristive neural networks: a gain-scheduled control scheme. IEEE Trans Neural Netw Learn Syst 30(6):1841\u20131853","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"10262_CR29","first-page":"485","volume":"48","author":"R Samidurai","year":"2016","unstructured":"Samidurai R, Manivannan R, Ahn CK et al (2016) New criteria for stability of generalized neural networks including Markov jump parameters and additive time delays. IEEE Trans Neural Netw Learn Syst 48(4):485\u2013499","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10262_CR30","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.neucom.2019.04.020","volume":"349","author":"L Liu","year":"2019","unstructured":"Liu L, He X, Wu A (2019) $$p$$th moment exponential input-to-state stability of non-autonomous delayed Cohen-Grossberg neural networks with Markovian switching. Neurocomputing 349:44\u201351","journal-title":"Neurocomputing"},{"key":"10262_CR31","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neunet.2016.08.001","volume":"84","author":"Z Li","year":"2016","unstructured":"Li Z, Liu L, Zhu Q (2016) Mean-square exponential input-to-state stability of delayed Cohen\u2013Grossberg neural networks with Markovian switching based on vector Lyapunov functions. Neural Netw 84:39\u201346","journal-title":"Neural Netw"},{"issue":"2","key":"10262_CR32","first-page":"509","volume":"47","author":"H Zhao","year":"2018","unstructured":"Zhao H, Li L, Peng H et al (2018) Finite-time robust synchronization of memrisive neural network with perturbation. Neural Process Lett 47(2):509\u2013533","journal-title":"Neural Process Lett"},{"key":"10262_CR33","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neunet.2019.07.004","volume":"118","author":"X Wan","year":"2019","unstructured":"Wan X, Yang X, Tang R et al (2019) Exponential synchronization of semi-Markovian coupled neural networks with mixed delays via tracker information and quantized output controller. Neural Netw 118:321\u2013331","journal-title":"Neural Netw"},{"issue":"2","key":"10262_CR34","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S0252-9602(18)30766-5","volume":"38","author":"P Baskar","year":"2018","unstructured":"Baskar P, Padmanabhan S, Ali MS (2018) Finite-time Hontrol control for a class of Markovian jumping neural networks with distributed time varying delays-LMI approach. Acta Mathematica Scientia 38(2):561\u2013579","journal-title":"Acta Mathematica Scientia"},{"issue":"10","key":"10262_CR35","doi-asserted-by":"crossref","first-page":"2166","DOI":"10.1109\/TSMC.2017.2766260","volume":"49","author":"X Li","year":"2019","unstructured":"Li X (2019) Global exponential stability of impulsive delay systems with flexible impulse frequency. IEEE Trans Syst Man Cybern Syst 49(10):2166\u20132174","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"10262_CR36","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.neucom.2013.10.018","volume":"131","author":"R Rakkiyappan","year":"2014","unstructured":"Rakkiyappan R, Chandrasekar A, Lakshmanan S et al (2014) Exponential stability of Markovian jumping stochastic CohenCGrossberg neural networks with mode-dependent probabilistic time-varying delays and impulses. Neurocomputing 131:265\u2013277","journal-title":"Neurocomputing"},{"key":"10262_CR37","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.matcom.2018.07.006","volume":"156","author":"Z Wang","year":"2019","unstructured":"Wang Z, Liu X (2019) Exponential stability of impulsive complex-valued neural networks with time delay. Math Comput Simul 156:143\u2013157","journal-title":"Math Comput Simul"},{"key":"10262_CR38","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.neunet.2019.08.011","volume":"119","author":"Y Cao","year":"2019","unstructured":"Cao Y, Wang S, Guo Z et al (2019) Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control. Neural Netw 119:178\u2013189","journal-title":"Neural Netw"},{"key":"10262_CR39","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.neucom.2018.06.018","volume":"314","author":"FX Wang","year":"2018","unstructured":"Wang FX, Liu XG, Li J (2018) Synchronization analysis for fractional non-autonomous neural networks by a Halanay inequality. Neurocomputing 314:20\u201329","journal-title":"Neurocomputing"},{"issue":"1","key":"10262_CR40","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TNNLS.2016.2614998","volume":"29","author":"DT Son","year":"2018","unstructured":"Son DT, Trinh H (2018) On global dissipativity of nonautonomous neural networks with multiple proportional delays. IEEE Trans Neural Netw Learn Syst 29(1):225\u2013231","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"10262_CR41","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s11063-018-9821-9","volume":"49","author":"M Syed Ali","year":"2019","unstructured":"Syed Ali M, Yogambigai J (2019) Synchronization criterion of complex dynamical networks with both leakage delay and coupling delay on time scales. Neural Process Lett 49(2):453\u2013466","journal-title":"Neural Process Lett"},{"key":"10262_CR42","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.neucom.2016.09.038","volume":"219","author":"L Zhou","year":"2017","unstructured":"Zhou L, Liu X (2017) Mean-square exponential input-to-state stability of stochastic recurrent neural networks with multi-proportional delays. Neurocomputing 219:396\u2013403","journal-title":"Neurocomputing"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10262-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-020-10262-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10262-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T00:01:50Z","timestamp":1621987310000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-020-10262-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,26]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["10262"],"URL":"https:\/\/doi.org\/10.1007\/s11063-020-10262-3","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2020,5,26]]},"assertion":[{"value":"26 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}