{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:44:53Z","timestamp":1784904293566,"version":"3.55.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Hubei University of Technology Doctoral Research Launch Fund","award":["XJ2022001501"],"award-info":[{"award-number":["XJ2022001501"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00521-024-09462-y","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T20:02:42Z","timestamp":1709150562000},"page":"7327-7341","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Quasi-projective synchronization of discrete-time BAM neural networks by discrete inequality techniques"],"prefix":"10.1007","volume":"36","author":[{"given":"Zhen","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengqiu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaying","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"9462_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.112948","volume":"166","author":"A Kumar","year":"2023","unstructured":"Kumar A, Das S, Singh S (2023) Rajeev, Quasi-projective synchronization of inertial complex-valued recurrent neural networks with mixed time-varying delay and mismatched parameters. Chaos Solitons Fractals 166:112948. https:\/\/doi.org\/10.1016\/j.chaos.2022.112948","journal-title":"Chaos Solitons Fractals"},{"key":"9462_CR2","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.ins.2022.08.106","volume":"612","author":"S Baluni","year":"2022","unstructured":"Baluni S, Yadav VK, Das S (2022) Quasi projective synchronization of time varying delayed complex valued Cohen-Grossberg neural networks. Inf Sci 612:231\u2013240. https:\/\/doi.org\/10.1016\/j.ins.2022.08.106","journal-title":"Inf Sci"},{"key":"9462_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.111323","volume":"152","author":"A Kumar","year":"2021","unstructured":"Kumar A, Das S, Yadav VK (2021) Rajeev, Global quasi-synchronization of complex-valued recurrent neural networks with time-varying delay and interaction terms. Chaos Solitons Fractals 152:111323. https:\/\/doi.org\/10.1016\/j.chaos.2021.111323","journal-title":"Chaos Solitons Fractals"},{"issue":"9","key":"9462_CR4","doi-asserted-by":"publisher","first-page":"5389","DOI":"10.1109\/TSMC.2021.3124158","volume":"52","author":"Z Wang","year":"2022","unstructured":"Wang Z, Jin X, Pan L, Feng Y, Cao J (2022) Quasi-synchronization of delayed stochastic multiplex networks via impulsive pinning control. IEEE Trans Syst Man Cybern Syst 52(9):5389\u20135397. https:\/\/doi.org\/10.1109\/TSMC.2021.3124158","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"9462_CR5","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.3390\/sym14051035","volume":"14","author":"R Ye","year":"2022","unstructured":"Ye R, Wang C, Shu A, Zhang H (2022) Quasi-synchronization and quasi-uniform synchronization of Caputo fractional variable-parameter neural networks with probabilistic time-varying delays. Symmetry 14:1035. https:\/\/doi.org\/10.3390\/sym14051035","journal-title":"Symmetry"},{"key":"9462_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.112121","volume":"159","author":"H Yan","year":"2022","unstructured":"Yan H, Qiao Y, Duan L, Miao J (2022) New results of quasi-projective synchronization for fractional-order complex-valued neural networks with leakage and discrete delays. Chaos Solitons Fractals 159:112121. https:\/\/doi.org\/10.1016\/j.chaos.2022.112121","journal-title":"Chaos Solitons Fractals"},{"key":"9462_CR7","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neunet.2022.02.023","volume":"150","author":"Y Xu","year":"2022","unstructured":"Xu Y, Liu J, Li W (2022) Quasi-synchronization of fractional-order multi-layer networks with mismatched parameters via delay-dependent impulsive feedback control. Neural Netw 150:43\u201357. https:\/\/doi.org\/10.1016\/j.neunet.2022.02.023","journal-title":"Neural Netw"},{"key":"9462_CR8","doi-asserted-by":"publisher","first-page":"260","DOI":"10.3390\/fractalfract5040260","volume":"5","author":"X Liu","year":"2021","unstructured":"Liu X, Li K, Song Q, Yang X (2021) Quasi-projective synchronization of distributed-order recurrent neural networks. Fractal Fract 5:260. https:\/\/doi.org\/10.3390\/fractalfract5040260","journal-title":"Fractal Fract"},{"key":"9462_CR9","doi-asserted-by":"publisher","unstructured":"Jin C, Shi S, Wang Z, He W, Jiang G-P (2021) Quasi-synchronization of heterogeneous Lur\u2019e networks with impulsive feedback control. In: 2021 40th Chinese control conference (CCC). pp 4792-4797. https:\/\/doi.org\/10.23919\/CCC52363.2021.9549391.","DOI":"10.23919\/CCC52363.2021.9549391."},{"issue":"7","key":"9462_CR10","doi-asserted-by":"publisher","first-page":"4379","DOI":"10.1109\/TSMC.2019.2930971","volume":"51","author":"Y Xu","year":"2021","unstructured":"Xu Y, Huang Z, Rao H, Lu R, Huang T (2021) Quasi-synchronization for periodic neural networks with asynchronous target and constrained information. IEEE Trans Syst Man Cybern Syst 51(7):4379\u20134388. https:\/\/doi.org\/10.1109\/TSMC.2019.2930971","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"9","key":"9462_CR11","doi-asserted-by":"publisher","first-page":"4121","DOI":"10.1109\/TCYB.2019.2941582","volume":"50","author":"H Rao","year":"2020","unstructured":"Rao H, Xu Y, Peng H, Lu R, Su C-Y (2020) Quasi-synchronization of time delay Markovian jump neural networks with impulsive-driven transmission and fading channels. IEEE Trans Cybern 50(9):4121\u20134131. https:\/\/doi.org\/10.1109\/TCYB.2019.2941582","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"9462_CR12","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.1109\/TSMC.2019.2911366","volume":"51","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Zhang H, Zeng Z (2021) Quasi-synchronization of delayed memristive neural networks via a Hybrid impulsive control. IEEE Trans Syst Man Cybern Syst 51(3):1954\u20131965. https:\/\/doi.org\/10.1109\/TSMC.2019.2911366","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"9462_CR13","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.neunet.2021.01.013","volume":"141","author":"J Chen","year":"2021","unstructured":"Chen J, Chen B, Zeng Z (2021) Exponential quasi-synchronization of coupled delayed memristive neural networks via intermittent event-triggered control. Neural Netw 141:98\u2013106. https:\/\/doi.org\/10.1016\/j.neunet.2021.01.013","journal-title":"Neural Netw"},{"key":"9462_CR14","doi-asserted-by":"publisher","first-page":"2482","DOI":"10.1016\/j.jfranklin.2021.01.023","volume":"358","author":"X Song","year":"2021","unstructured":"Song X, Li X, Song S, Zhang Y, Ning Z (2021) Quasi-synchronization of coupled neural networks with reaction-diffusion terms driven by fractional brownian motion. J Franklin Inst 358:2482\u20132499. https:\/\/doi.org\/10.1016\/j.jfranklin.2021.01.023","journal-title":"J Franklin Inst"},{"key":"9462_CR15","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2020.05.037","volume":"409","author":"J Cai","year":"2020","unstructured":"Cai J, Feng J, Wang J, Zhao Y (2020) Quasi-synchronization of neural networks with diffusion effects via intermittent control of regional division. Neurocomputing 409:146\u2013156. https:\/\/doi.org\/10.1016\/j.neucom.2020.05.037","journal-title":"Neurocomputing"},{"key":"9462_CR16","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.1109\/TCSI.2003.816306","volume":"50","author":"Z Liu","year":"2003","unstructured":"Liu Z, Chen A, Cao J, Huang L (2003) Existence and global exponential stability of periodic solution for BAM neural networks with periodic coefficients and time-varying delays. IEEE Trans Circuits Syst 50:1162\u20131173","journal-title":"IEEE Trans Circuits Syst"},{"key":"9462_CR17","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.chaos.2004.03.004","volume":"22","author":"J Liang","year":"2004","unstructured":"Liang J, Cao J (2004) Exponential stability of continuous-time and discrete-time bidirectional associative memory networks with delays. Chaos Solitons Fractals 22:773\u2013785","journal-title":"Chaos Solitons Fractals"},{"key":"9462_CR18","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/S0252-9602(17)30008-5","volume":"37","author":"MS Ali","year":"2017","unstructured":"Ali MS, Yogamnigai J, Cao J (2017) Synchronization of master-slave Markovian switching complex dynamical networks with time-varying delays in nonlinear function via sliding mode control. Acta Math Sci 37:368\u2013384","journal-title":"Acta Math Sci"},{"key":"9462_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0022-0000(92)90038-K","volume":"45","author":"X Wang","year":"1992","unstructured":"Wang X, Blum EK (1992) Discrete-time versus continuous-time models of neural networks. J Comput Syst Sci 45:1\u201319","journal-title":"J Comput Syst Sci"},{"issue":"4","key":"9462_CR20","doi-asserted-by":"publisher","first-page":"2290","DOI":"10.1109\/TCYB.2019.2937526","volume":"51","author":"Q Xiao","year":"2021","unstructured":"Xiao Q, Huang T (2021) Quasi-synchronization of discrete-time inertial neural networks with parameter mismatches and delays. IEEE Trans Cybern 51(4):2290\u20132295. https:\/\/doi.org\/10.1109\/TCYB.2019.2937526","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"9462_CR21","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1109\/TCYB.2020.3004894","volume":"51","author":"S Ding","year":"2021","unstructured":"Ding S, Wang Z, Rong N (2021) Intermittent control for quasi-synchronization of delayed discrete-time neural networks. IEEE Trans Cybern 51(2):862\u2013873. https:\/\/doi.org\/10.1109\/TCYB.2020.3004894","journal-title":"IEEE Trans Cybern"},{"key":"9462_CR22","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/CAC.2018.8623671","volume":"2018","author":"H Rao","year":"2018","unstructured":"Rao H, Gan Y, Xu Y, Liu F (2018) Quasi-synchronization of discrete-time neural networks with randomly occurred uncertainty and parameter mismatch. Chin Autom Congress (CAC) 2018:374\u2013378. https:\/\/doi.org\/10.1109\/CAC.2018.8623671","journal-title":"Chin Autom Congress (CAC)"},{"issue":"4","key":"9462_CR23","doi-asserted-by":"publisher","first-page":"158","DOI":"10.12677\/dsc.2017.64020","volume":"6","author":"J Li","year":"2017","unstructured":"Li J, Bin H, Huang Z (2017) Quasi-synchronization of discrete-time networks with parameter mismatches under impulsive control. Dyn Syst Control 6(4):158\u2013163. https:\/\/doi.org\/10.12677\/dsc.2017.64020","journal-title":"Dyn Syst Control"},{"key":"9462_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2020.125851","volume":"395","author":"R Li","year":"2021","unstructured":"Li R, Cao J, Xue C, Manivannan R (2021) Quasi-stability and quasi-synchronization control of quaternion-valued fractional-order discrete-time memristive neural networks. Appl Math Comput 395:125851. https:\/\/doi.org\/10.1016\/j.amc.2020.125851","journal-title":"Appl Math Comput"},{"key":"9462_CR25","doi-asserted-by":"publisher","first-page":"4615","DOI":"10.1007\/s00521-018-3772-y","volume":"32","author":"Y Song","year":"2020","unstructured":"Song Y, Zeng Z, Sun W et al (2020) Quasi-synchronization of stochastic memristor-based neural networks with mixed delays and parameter mismatches. Neural Comput Appl 32:4615\u20134628","journal-title":"Neural Comput Appl"},{"key":"9462_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.nahs.2022.101291","volume":"47","author":"Z Dong","year":"2023","unstructured":"Dong Z, Wang X, Zhang X, Hu M, Dinh TN (2023) Global exponential synchronization of discrete-time high-order switched neural networks and its application to multi-channel audio encryption. Nonlinear Anal Hybrid Syst 47:101291. https:\/\/doi.org\/10.1016\/j.nahs.2022.101291","journal-title":"Nonlinear Anal Hybrid Syst"},{"key":"9462_CR27","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.neucom.2022.09.035","volume":"511","author":"S Chen","year":"2022","unstructured":"Chen S, Li H-L, Bao H, Zhang L, Jiang H, Li Z (2022) Global Mittag-Leffler stability and synchronization of discrete-time fractional-order delayed quaternion-valued neural networks. Neurocomputing 511:290\u2013298. https:\/\/doi.org\/10.1016\/j.neucom.2022.09.035","journal-title":"Neurocomputing"},{"key":"9462_CR28","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.neunet.2022.02.028","volume":"150","author":"Y Liu","year":"2022","unstructured":"Liu Y, Shen B, Zhang P (2022) Synchronization and state estimation for discrete-time coupled delayed complex-valued neural networks with random system parameters. Neural Netw 150:181\u2013193. https:\/\/doi.org\/10.1016\/j.neunet.2022.02.028","journal-title":"Neural Netw"},{"issue":"9","key":"9462_CR29","doi-asserted-by":"publisher","first-page":"4191","DOI":"10.1109\/TNNLS.2020.3017171","volume":"32","author":"X Yang","year":"2021","unstructured":"Yang X, Wan X, Zunshui C, Cao J, Liu Y, Rutkowski L (2021) Synchronization of switched discrete-time neural networks via quantized output control with actuator fault. IEEE Trans Neural Netw Learn Syst 32(9):4191\u20134201. https:\/\/doi.org\/10.1109\/TNNLS.2020.3017171","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9462_CR30","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.neucom.2020.01.110","volume":"392","author":"H Li","year":"2020","unstructured":"Li H, Fang J, Li X, Huang T (2020) Exponential synchronization of multiple impulsive discrete-time memristor-based neural networks with stochastic perturbations and time-varying delays. Neurocomputing 392:86\u201397. https:\/\/doi.org\/10.1016\/j.neucom.2020.01.110","journal-title":"Neurocomputing"},{"key":"9462_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2019.124995","volume":"372","author":"Y Gu","year":"2020","unstructured":"Gu Y, Wang H, Yu Y (2020) Synchronization for fractional-order discrete-time neural networks with time delays. Appl Math Comput 372:124995. https:\/\/doi.org\/10.1016\/j.amc.2019.124995","journal-title":"Appl Math Comput"},{"key":"9462_CR32","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.neunet.2019.11.004","volume":"122","author":"X You","year":"2020","unstructured":"You X, Song Q, Zhao Z (2020) Global Mittag-Leffler stability and synchronization of discrete-time fractional-order complex-valued neural networks with time delay. Neural Netw 122:382\u2013394. https:\/\/doi.org\/10.1016\/j.neunet.2019.11.004","journal-title":"Neural Netw"},{"issue":"6","key":"9462_CR33","doi-asserted-by":"publisher","first-page":"1854","DOI":"10.1109\/TNNLS.2018.2874982","volume":"30","author":"Q Xiao","year":"2019","unstructured":"Xiao Q, Huang T, Zeng Z (2019) Global exponential stability and synchronization for discrete-time inertial neural networks with time delays: a timescale approach. IEEE Trans Neural Netw Learn Syst 30(6):1854\u20131866. https:\/\/doi.org\/10.1109\/TNNLS.2018.2874982","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9462_CR34","doi-asserted-by":"publisher","unstructured":"Fu Q, Cai J, Zhong S, Yu Y (2018) Exponential synchronization for discrete-time memristive neural networks with time-varying delays. In: IEEE international conference on communication systems (ICCS), vol. 2018, pp 423\u2013426. https:\/\/doi.org\/10.1109\/ICCS.2018.8689219","DOI":"10.1109\/ICCS.2018.8689219"},{"key":"9462_CR35","doi-asserted-by":"publisher","first-page":"52073","DOI":"10.1109\/ACCESS.2018.2870729","volume":"6","author":"D-H Lin","year":"2018","unstructured":"Lin D-H, Wu J, Li J-N, Cai J-P (2018) Asymptotic synchronization control of discrete-time delayed neural networks with a reuse mechanism under missing data and uncertainty. IEEE Access 6:52073\u201352081. https:\/\/doi.org\/10.1109\/ACCESS.2018.2870729","journal-title":"IEEE Access"},{"key":"9462_CR36","doi-asserted-by":"publisher","unstructured":"Lin D-H, Wu J, Li J-N, Cai J-P (2017) Asymptotical synchronization control of discrete-time neural networks with time-varying delays and controller nonlinearity. In: 2017 32nd youth academic annual conference of Chinese association of automation (YAC), pp 998\u20131003. https:\/\/doi.org\/10.1109\/YAC.2017.7967556.","DOI":"10.1109\/YAC.2017.7967556."},{"key":"9462_CR37","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.neunet.2022.02.028","volume":"150","author":"Y Liu","year":"2022","unstructured":"Liu Y, Shen B, Zhang P (2022) Synchronization and state estimation for discrete-time coupled delayed complex-valued neural networks with random system parameters. Neural Netw 150:181\u2013193","journal-title":"Neural Netw"},{"key":"9462_CR38","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.neucom.2017.08.069","volume":"275","author":"P Wang","year":"2018","unstructured":"Wang P, Chen ZR, Li WX (2018) Graph-theoretic approach to exponential synchronization of discrete-time stochastic coupled systems with time-varying delay. Neurocomputing 275:659\u2013666","journal-title":"Neurocomputing"},{"key":"9462_CR39","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.neucom.2016.05.056","volume":"207","author":"J Li","year":"2016","unstructured":"Li J, Bao WD, Li SB, Wen CL, Li LS (2016) Exponential synchronization of discrete-time mixed delay neural networks with actuator constraints and stochastic missing data. Neurocomputing 207:700\u2013707","journal-title":"Neurocomputing"},{"key":"9462_CR40","doi-asserted-by":"publisher","first-page":"10503","DOI":"10.1007\/s00521-021-05808-y","volume":"33","author":"X Liu","year":"2021","unstructured":"Liu X, Yu Y (2021) Synchronization analysis for discrete fractional-order complex-valued neural networks with time delays. Neural Comput Appl 33:10503\u201310514","journal-title":"Neural Comput Appl"},{"key":"9462_CR41","doi-asserted-by":"publisher","first-page":"4947","DOI":"10.1364\/AO.26.004947","volume":"26","author":"B Kosko","year":"1987","unstructured":"Kosko B (1987) Adaptive bidirectional associative memories. Appl Opt 26:4947\u20134960","journal-title":"Appl Opt"},{"key":"9462_CR42","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/21.87054","volume":"18","author":"B Kosko","year":"1988","unstructured":"Kosko B (1988) Bidirectional associative memories. IEEE Trans Syst Man Cybern Syst 18:49\u201360","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"6","key":"9462_CR43","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1007\/s11571-021-09778-8","volume":"16","author":"C Yang","year":"2022","unstructured":"Yang C, Liu Y, Huang L (2022) Finite-time and fixed-time stabilization of multiple memristive neural networks with nonlinear coupling. Cogn Neurodyn 16(6):1471\u20131483","journal-title":"Cogn Neurodyn"},{"key":"9462_CR44","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neunet.2018.04.007","volume":"104","author":"S Yang","year":"2018","unstructured":"Yang S, Yu J, Hu C et al (2018) Quasi-projective synchronization of fractional-order complex-valued recurrent neural networks. Neural Netw 104:104\u2013113","journal-title":"Neural Netw"},{"key":"9462_CR45","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.neucom.2019.05.012","volume":"356","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Zheng T, Yu S (2019) Finite-time anti-synchronization of neural networks with time-varying delays via inequality skills. Neurocomputing 356:60\u201368","journal-title":"Neurocomputing"},{"key":"9462_CR46","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.neunet.2019.04.008","volume":"116","author":"C Yang","year":"2019","unstructured":"Yang C, Huang L, Cai Z (2019) Fixed-time synchronization of coupled memristor-based neural networks with time-varying delays. Neural Netw 116:101\u2013109","journal-title":"Neural Netw"},{"issue":"4","key":"9462_CR47","first-page":"1555","volume":"70","author":"G Yang","year":"2023","unstructured":"Yang G, Tong D, Chen Q, Zhou W (2023) Fixed-time synchronization and energy consumption for Kuramoto-oscillator networks with multilayer distributed control. IEEE Trans Circuits Syst II Express Briefs 70(4):1555\u20131559","journal-title":"IEEE Trans Circuits Syst II Express Briefs"},{"issue":"6","key":"9462_CR48","first-page":"2176","volume":"70","author":"D Tong","year":"2023","unstructured":"Tong D, Ma B, Chen Q, Wei Y, Shi P (2023) Finite-time synchronization and energy consumption prediction for multilayer fractional-order networks. IEEE Trans Circuits Syst II Express Briefs 70(6):2176\u20132180","journal-title":"IEEE Trans Circuits Syst II Express Briefs"},{"issue":"5","key":"9462_CR49","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1109\/TNNLS.2018.2868800","volume":"30","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Cao J (2019) Novel finite-time synchronization criteria for inertial neural networks with time delays via integral inequality method. IEEE Trans Neural Netw Learn Syst 30(5):1476\u20131485. https:\/\/doi.org\/10.1109\/TNNLS.2018.2868800","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"9462_CR50","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1109\/TFUZZ.2021.3059953","volume":"30","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Cao J (2022) Finite-time synchronization for fuzzy inertial neural networks by maximum value approach. IEEE Trans Fuzzy Syst 30(5):1436\u20131446. https:\/\/doi.org\/10.1109\/TFUZZ.2021.3059953","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"8","key":"9462_CR51","doi-asserted-by":"publisher","first-page":"3085","DOI":"10.1109\/TFUZZ.2021.3103597","volume":"30","author":"T Chen","year":"2022","unstructured":"Chen T, Wang W, Wu Y (2022) Quasi-synchronization of fuzzy heterogeneous complex networks via intermittent discrete-time state observations control. IEEE Trans Fuzzy Syst 30(8):3085\u20133097. https:\/\/doi.org\/10.1109\/TFUZZ.2021.3103597","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"9462_CR52","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neucom.2019.09.034","volume":"373","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Chen M, Li A (2020) Further study on finite-time synchronization for delayed inertial neural networks via inequality skills. Neurocomputing 373:15\u201323","journal-title":"Neurocomputing"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09462-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09462-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09462-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T13:22:41Z","timestamp":1711027361000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09462-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":52,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9462"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09462-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,28]]},"assertion":[{"value":"31 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflict of interest exits in the submission of this paper which is approved by all authors for publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}