{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T23:59:52Z","timestamp":1774310392601,"version":"3.50.1"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1016\/j.neucom.2019.12.038","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T10:44:08Z","timestamp":1576493048000},"page":"115-129","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":20,"special_numbering":"C","title":["Finite-time event-triggered non-fragile state estimation for discrete-time delayed neural networks with randomly occurring sensor nonlinearity and energy constraints"],"prefix":"10.1016","volume":"384","author":[{"given":"Yamin","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arunkumar","family":"Arumugam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-8035-288X","authenticated-orcid":false,"given":"Yurong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuad E.","family":"Alsaadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2019.12.038_bib0001","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.physa.2019.01.062","article-title":"Delay driven vegetation patterns of a plankton system on a network","volume":"521","author":"Bao","year":"2019","journal-title":"Phys. A: Stat. Mech. Appl."},{"key":"10.1016\/j.neucom.2019.12.038_bib0002","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neucom.2019.04.034","article-title":"Further improved results on non-fragile H\u221e performance state estimation for delayed static neural networks","volume":"356","author":"Dong","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.12.038_bib0003","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.fss.2018.04.017","article-title":"Finite-time synchronization of delayed fuzzy cellular neural networks with discontinuous activations","volume":"361","author":"Duan","year":"2019","journal-title":"Fuzzy Sets Syst."},{"key":"10.1016\/j.neucom.2019.12.038_bib0004","doi-asserted-by":"crossref","first-page":"104655","DOI":"10.1109\/ACCESS.2019.2931714","article-title":"Further stability analysis for time-delayed neural networks based on an augmented Lyapunov functional","volume":"7","author":"Duan","year":"2019","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.neucom.2019.12.038_bib0005","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1007\/s12555-018-0138-2","article-title":"Improved sufficient LMI conditions for the robust stability of time-delayed neutral-type Lur\u2019e systems","volume":"16","author":"Duan","year":"2018","journal-title":"Int. J. Control Autom. Syst."},{"issue":"2","key":"10.1016\/j.neucom.2019.12.038_bib0006","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1109\/TNNLS.2015.2411290","article-title":"Optimal communication network-based H\u221e quantized control with packet dropouts for a class of discrete-time neural networks with distributed time delay","volume":"27","author":"Han","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2019.12.038_bib0007","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neucom.2015.11.089","article-title":"Non-fragile state estimation for discrete Markovian jumping neural networks","volume":"179","author":"Hou","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.12.038_bib0008","doi-asserted-by":"crossref","unstructured":"J. Hu, Z. Wang, G. Liu, H. Zhang, Variance-constrained recursive state estimation for time-varying complex networks with quantized measurements and uncertain inner coupling, IEEE Trans. Neural Netw. Learn. Syst. doi:10.1109\/TNNLS.2019.2927554.","DOI":"10.1109\/TNNLS.2019.2927554"},{"key":"10.1016\/j.neucom.2019.12.038_bib0009","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.automatica.2015.11.008","article-title":"A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements","volume":"64","author":"Hu","year":"2016","journal-title":"Automatica"},{"key":"10.1016\/j.neucom.2019.12.038_bib0010","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.neucom.2016.01.022","article-title":"Event-triggered network-based synchronization of delayed neural networks","volume":"190","author":"Lang","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.12.038_bib0011","first-page":"205","article-title":"Network-based H\u221e state estimation for neural networks using imperfect measurement","volume":"316","author":"Lee","year":"2018","journal-title":"Appl. Math. Comput."},{"issue":"3","key":"10.1016\/j.neucom.2019.12.038_bib0012","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1016\/j.jfranklin.2018.10.032","article-title":"Finite-time non-fragile state estimation for discrete neural networks with sensor failures, time-varying delays and randomly occurring sensor nonlinearity","volume":"356","author":"Li","year":"2019","journal-title":"J. Frankl. Inst."},{"key":"10.1016\/j.neucom.2019.12.038_bib0013","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.cam.2016.09.022","article-title":"Finite-time fault detection filter design for discrete-time interconnected systems with average dwell time","volume":"313","author":"Li","year":"2017","journal-title":"Appl. Math. Comput."},{"key":"10.1016\/j.neucom.2019.12.038_bib0014","doi-asserted-by":"crossref","unstructured":"L. Li, W. Zou, S. Fei, Event-triggered synchronization of delayed neural networks with actuator saturation using quantized measurements, J. Frankl. Inst. doi:10.1016\/j.jfranklin.2019.02.037.","DOI":"10.1016\/j.jfranklin.2019.02.037"},{"key":"10.1016\/j.neucom.2019.12.038_bib0015","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.neucom.2019.02.051","article-title":"Finite-time synchronization of memristive neural networks with discontinuous activation functions and mixed time-varying delays","volume":"340","author":"Li","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.12.038_bib0016","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.neunet.2018.09.011","article-title":"Reachable set estimation for markovian jump neural networks with time-varying delay","volume":"108","author":"Lin","year":"2018","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2019.12.038_bib0017","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neucom.2018.02.060","article-title":"Quantized state estimation for neural networks with cyber attacks and hybrid triggered communication scheme","volume":"291","author":"Liu","year":"2018","journal-title":"Neurocomputing"},{"issue":"2","key":"10.1016\/j.neucom.2019.12.038_bib0018","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40815-018-0590-4","article-title":"Event-triggered state estimation for t-s fuzzy neural networks with stochastic cyber-attacks","volume":"21","author":"Liu","year":"2019","journal-title":"Int. J. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2019.12.038_bib0019","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.inffus.2018.12.011","article-title":"A partial-nodes-based information fusion approach to state estimation for discrete-time delayed stochastic complex networks","volume":"49","author":"Liu","year":"2019","journal-title":"Inf. Fusion"},{"issue":"14","key":"10.1016\/j.neucom.2019.12.038_bib0020","doi-asserted-by":"crossref","first-page":"6339","DOI":"10.1016\/j.jfranklin.2018.06.024","article-title":"Robust H\u221e control for a class of uncertain nonlinear systems with mixed time-delays","volume":"355","author":"Liu","year":"2018","journal-title":"J. Frankl. Inst."},{"issue":"8","key":"10.1016\/j.neucom.2019.12.038_bib0021","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1109\/TNNLS.2017.2740400","article-title":"Partial-nodes-based state estimation for complex networks with unbounded distributed delays","volume":"29","author":"Liu","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"6","key":"10.1016\/j.neucom.2019.12.038_bib0022","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TSMC.2017.2720121","article-title":"Event-triggered partial-nodes-based state estimation for delayed complex networks with bounded distributed delays","volume":"49","author":"Liu","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern.-Syst."},{"issue":"5","key":"10.1016\/j.neucom.2019.12.038_bib0023","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.neunet.2005.03.015","article-title":"Global exponential stability of generalized recurrent neural networks with discrete and distributed delays","volume":"19","author":"Liu","year":"2006","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2019.12.038_bib0024","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.fss.2015.10.007","article-title":"Robust reliable H\u221e control for fuzzy systems with random delays and linear fractional uncertainties","volume":"302","author":"Sakthivel","year":"2016","journal-title":"Fuzzy Sets Syst."},{"key":"10.1016\/j.neucom.2019.12.038_bib0025","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neucom.2018.10.020","article-title":"Finite-time leaderless consensus of uncertain multi-agent systems against time-varying actuator faults","volume":"325","author":"Sakthivel","year":"2019","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.neucom.2019.12.038_bib0026","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1049\/iet-syb.2016.0040","article-title":"Non-fragile reliable control synthesis of the sugarcane borer","volume":"11","author":"Sakthivel","year":"2017","journal-title":"IET Syst. Biol."},{"key":"10.1016\/j.neucom.2019.12.038_bib0027","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isatra.2018.08.017","article-title":"Dissipativity-based non-fragile sampled-data control design of interval type-2 fuzzy systems subject to random delays","volume":"83","author":"Sakthivel","year":"2018","journal-title":"ISA Trans."},{"key":"10.1016\/j.neucom.2019.12.038_bib0028","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.fss.2018.01.017","article-title":"Finite-time H\u221e asynchronous state estimation for discrete-time fuzzy Markov jump neural networks with uncertain measurements","volume":"356","author":"Shen","year":"2019","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"10.1016\/j.neucom.2019.12.038_bib0029","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.neucom.2014.09.059","article-title":"Stochastic finite-time state estimation for discrete time-delay neural networks with Markovian jumps","volume":"151","author":"Shi","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.12.038_bib0030","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.neucom.2017.08.027","article-title":"Further results on l2\u2212l\u221e state estimation of delayed neural networks","volume":"273","author":"Qian","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.12.038_bib0031","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.sigpro.2018.07.027","article-title":"Adaptive event-triggered H\u221e filtering for discrete-time delayed neural networks with randomly occurring missing measurements","volume":"153","author":"Wang","year":"2018","journal-title":"Signal Process."},{"key":"10.1016\/j.neucom.2019.12.038_bib0032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2018.11.022","article-title":"Exponential stability criterion of the switched neural networks with time-varying delay","volume":"331","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.neucom.2019.12.038_bib0033","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/TNNLS.2015.2411734","article-title":"Event-triggered generalized dissipativity filtering for neural networks with time-varying delays","volume":"27","author":"Wang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"8","key":"10.1016\/j.neucom.2019.12.038_bib0034","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1109\/TCYB.2017.2740309","article-title":"Synchronization control for a class of discrete-time dynamical networks with packet dropouts: a coding-decoding-based approach","volume":"48","author":"Wang","year":"2018","journal-title":"IEEE Trans. Cybern."},{"issue":"12","key":"10.1016\/j.neucom.2019.12.038_bib0035","doi-asserted-by":"crossref","first-page":"4335","DOI":"10.1109\/TCYB.2018.2863664","article-title":"Observer-based consensus control for discrete-time multiagent systems with coding-decoding communication protocol","volume":"49","author":"Wang","year":"2019","journal-title":"IEEE Trans. Cybern."},{"issue":"3","key":"10.1016\/j.neucom.2019.12.038_bib0036","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TNNLS.2015.2490168","article-title":"Finite-time state estimation for coupled Markovian neural networks with sensor nonlinearities","volume":"28","author":"Wang","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2019.12.038_bib0037","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.fss.2013.07.009","article-title":"Robust adaptive sliding-mode control of condenser-cleaning mobile manipulator using fuzzy wavelet neural network","volume":"235","author":"Wu","year":"2014","journal-title":"Fuzzy Sets Syst."},{"key":"10.1016\/j.neucom.2019.12.038_bib0038","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.neucom.2016.09.049","article-title":"Finite-time Mittag-Leffler synchronization of fractional-order memristive BAM neural networks with time delays","volume":"219","author":"Xiao","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.12.038_bib0039","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.neucom.2016.04.002","article-title":"Nonfragile l2\u2212l\u221e state estimation for discrete-time neural networks with jumping saturations","volume":"207","author":"Xu","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.12.038_bib0040","doi-asserted-by":"crossref","unstructured":"H. Yan, H. Zhang, F. Yang, X. Zhan, C. Peng, Event-triggered asynchronous guaranteed cost control for markov jump discrete-time neural networks with distributed delay and channel fading, IEEE Trans. Neural Netw. Learn. Syst. doi:10.1109\/TNNLS.2017.2732240.","DOI":"10.1109\/TNNLS.2017.2732240"},{"key":"10.1016\/j.neucom.2019.12.038_bib0041","doi-asserted-by":"crossref","unstructured":"L. Zha, J. Fang, J. Liu, E. Tian, Event-triggered non-fragile state estimation for delayed neural networks with randomly occurring sensor nonlinearity, Neurocomputing. doi:10.1016\/j.neucom.2017.08.011.","DOI":"10.1016\/j.neucom.2017.08.011"},{"key":"10.1016\/j.neucom.2019.12.038_bib0042","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.ins.2018.04.018","article-title":"Decentralized event-triggered H\u221e control for neural networks subject to cyber-attacks","volume":"457\u2013458","author":"Zha","year":"2018","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2019.12.038_bib0043","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.ins.2016.05.006","article-title":"Distributed non-fragile filtering in sensor networks with energy constraints","volume":"370\u2013371","author":"Zhang","year":"2016","journal-title":"Inf. Sci."},{"issue":"7","key":"10.1016\/j.neucom.2019.12.038_bib0044","first-page":"1618","article-title":"Energy-efficient distributed filtering in sensor networks: a unified switched system approach","volume":"47","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.neucom.2019.12.038_bib0045","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2018.07.086","article-title":"Recursive state estimation for time-varying complex networks subject to missing measurements and stochastic inner coupling under random access protocol","volume":"346","author":"Zhang","year":"2019","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.neucom.2019.12.038_bib0046","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s40815-017-0321-2","article-title":"An interval-valued fuzzy cerebellar model neural network based on intuitionistic fuzzy sets","volume":"19","author":"Zhao","year":"2017","journal-title":"Int. J. Fuzzy Syst."},{"issue":"2","key":"10.1016\/j.neucom.2019.12.038_bib0047","first-page":"720","article-title":"Recursive filtering for time-varying systems with random access protocol","volume":"64","author":"Zou","year":"2019","journal-title":"IEEE Trans. Autom. Control"},{"issue":"12","key":"10.1016\/j.neucom.2019.12.038_bib0048","doi-asserted-by":"crossref","first-page":"6582","DOI":"10.1109\/TAC.2017.2713353","article-title":"Ultimate boundedness control for networked systems with try-once-discard protocol and uniform quantization effects","volume":"62","author":"Zou","year":"2017","journal-title":"IEEE Trans. Autom. Control"},{"issue":"5","key":"10.1016\/j.neucom.2019.12.038_bib0049","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1109\/TNNLS.2016.2524621","article-title":"State estimation for discrete-time dynamical networks with time-varying delays and stochastic disturbances under the round-robin protocol","volume":"28","author":"Zou","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0925231219317412?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0925231219317412?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T18:52:13Z","timestamp":1760381533000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231219317412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4]]},"references-count":49,"alternative-id":["S0925231219317412"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.neucom.2019.12.038","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2020,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Finite-time event-triggered non-fragile state estimation for discrete-time delayed neural networks with randomly occurring sensor nonlinearity and energy constraints","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.neucom.2019.12.038","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}