{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T05:20:41Z","timestamp":1783056041680,"version":"3.54.6"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010712","name":"Viet Nam National University Ho Chi Minh City","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010712","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007224","name":"National Foundation for Science and Technology Development","doi-asserted-by":"publisher","award":["MDT 107.01-2025.12"],"award-info":[{"award-number":["MDT 107.01-2025.12"]}],"id":[{"id":"10.13039\/100007224","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s00521-026-12152-6","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T04:37:41Z","timestamp":1780029461000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive online estimation of hyper-chaotic plants using prescribed performance neural learning approach"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7353-8205","authenticated-orcid":false,"given":"Ho Pham Huy","family":"Anh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nguyen Tien","family":"Dat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"12152_CR1","unstructured":"Ljung L.(1999) Identification Toolbox \u2013 User\u2019s Manual, MATLAB platform: Prentice-Hall International."},{"key":"12152_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2023.128289","author":"Z Zhai","year":"2024","unstructured":"Zhai Z, Yan H, Chen S, Chang Y, Zhou J (2024) Novel stability criteria of generalized neural networks with time-varying delay based on the same augmented LKF and bounding technique. Appl Math Comput. https:\/\/doi.org\/10.1016\/j.amc.2023.128289","journal-title":"Appl Math Comput"},{"issue":"7","key":"12152_CR3","doi-asserted-by":"publisher","first-page":"481","DOI":"10.48550\/arXiv.2003.14162","volume":"54","author":"D Gedon","year":"2021","unstructured":"Gedon D, Wahlstr\u00f6m N, Sch\u00f6n TB, Ljung L (2021) Deep state space models for nonlinear system identification. IFAC-PapersOnLine 54(7):481\u2013486. https:\/\/doi.org\/10.48550\/arXiv.2003.14162","journal-title":"IFAC-PapersOnLine"},{"key":"12152_CR4","unstructured":"Beintema G, Toth R, Schoukens M (2021, May). \u201cNonlinear state-space identification using deep encoder neural networks\u201d, In\u00a0Learning for Dynamics and Control\u00a0(pp. 241\u2013250). PMLR."},{"key":"12152_CR5","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.matcom.2023.08.004","volume":"215","author":"X Liu","year":"2024","unstructured":"Liu X, Li M, Zeng P (2024) Adaptive finite-time neural network control for nonlinear stochastic systems with state constraints. Math Comput Simul 215:82\u201397. https:\/\/doi.org\/10.1016\/j.matcom.2023.08.004","journal-title":"Math Comput Simul"},{"key":"12152_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/72.80202","author":"K Narendra","year":"1990","unstructured":"Narendra K et al (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Net. https:\/\/doi.org\/10.1109\/72.80202","journal-title":"IEEE Trans Neural Net"},{"key":"12152_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechmachtheory.2022.105223","author":"J Ma","year":"2023","unstructured":"Ma J, Wang J, Han Y, Dong S (2023) Towards data-driven modeling for complex contact phenomena via self-optimized artificial neural network methodology. Mech Mach Theory. https:\/\/doi.org\/10.1016\/j.mechmachtheory.2022.105223","journal-title":"Mech Mach Theory"},{"issue":"1","key":"12152_CR8","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1007\/s10489-021-03106-7","volume":"53","author":"L Jia","year":"2022","unstructured":"Jia L, Li W, Qiao J (2022) An online adjusting RBF neural network for nonlinear system modeling. Appl Intell 53(1):440\u2013453. https:\/\/doi.org\/10.1007\/s10489-021-03106-7","journal-title":"Appl Intell"},{"key":"12152_CR9","doi-asserted-by":"publisher","unstructured":"Han Q. et al. (2024). \u201cDM-CNN: Dynamic Multi-scale Convolutional Neural Network with uncertainty quantification for medical image classification\u201d, Computers in Biology and Medicine, vol. 168. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.10775","DOI":"10.1016\/j.compbiomed.2023.10775"},{"key":"12152_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10030308","author":"S Mekruksavanich","year":"2021","unstructured":"Mekruksavanich S, Jitpattanakul A (2021) Biometric user identification based on human activity recognition using wearable sensors: an experiment using deep learning models. Electronics. https:\/\/doi.org\/10.3390\/electronics10030308","journal-title":"Electronics"},{"key":"12152_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2023.113717","author":"FE Alsaadi","year":"2023","unstructured":"Alsaadi FE, Jahanshahi H, Yao Q, Mou J (2023) Recurrent neural network-based technique for synchronization of fractional-order systems subject to control input limitations and faults. Chaos Solitons Fractals. https:\/\/doi.org\/10.1016\/j.chaos.2023.113717","journal-title":"Chaos Solitons Fractals"},{"key":"12152_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2023.113809","author":"H Cheng","year":"2023","unstructured":"Cheng H, Li H, Dai Q, Yang J (2023) A deep reinforcement learning method to control chaos synchronization between two identical chaotic systems. Chaos Solitons Fractals. https:\/\/doi.org\/10.1016\/j.chaos.2023.113809","journal-title":"Chaos Solitons Fractals"},{"key":"12152_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2023.113643","author":"L Zhou","year":"2023","unstructured":"Zhou L, Lin H, Tan F (2023) Fixed\/predefined-time synchronization of coupled memristor-based neural networks with stochastic disturbance. Chaos Solitons Fractals. https:\/\/doi.org\/10.1016\/j.chaos.2023.113643","journal-title":"Chaos Solitons Fractals"},{"issue":"8","key":"12152_CR14","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1016\/j.asoc.2012.02.017","volume":"12","author":"D Adhyaru","year":"2012","unstructured":"Adhyaru D (2012) State observer design for uncertain systems using neural networks. Appl Soft Comput 12(8):2530\u20132537. https:\/\/doi.org\/10.1016\/j.asoc.2012.02.017","journal-title":"Appl Soft Comput"},{"key":"12152_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110543","author":"A Cheng","year":"2023","unstructured":"Cheng A, Low YM (2023) Improved generalization of NARX neural networks for enhanced meta-modeling of nonlinear dynamic systems under stochastic excitations. Mech Syst Signal Process. https:\/\/doi.org\/10.1016\/j.ymssp.2023.110543","journal-title":"Mech Syst Signal Process"},{"key":"12152_CR16","doi-asserted-by":"crossref","unstructured":"Naouai T. et al. (2022). \u201cAdvanced control with extended Kalman filter and disturbance observer\u201d, Indonesian Journal of Electrical Engineering and Computer Science 28(1), p.124\u2013136","DOI":"10.11591\/ijeecs.v28.i1.pp124-136"},{"issue":"9","key":"12152_CR17","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.simpat.2008.06.009","volume":"16","author":"A Mezouar","year":"2008","unstructured":"Mezouar A et al (2008) Adaptive sliding mode observer for sensorless induction motor drive using two-time-scale approach. Simul Model Pract Theory 16(9):1323\u20131336. https:\/\/doi.org\/10.1016\/j.simpat.2008.06.009","journal-title":"Simul Model Pract Theory"},{"key":"12152_CR18","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.neucom.2016.01.015","volume":"190","author":"Z-J Fu","year":"2016","unstructured":"Fu Z-J et al (2016) Robust Adaptive Nonlinear Observer Design via Multi-Time scaled neural networks. Neuro-computing 190:217\u2013225. https:\/\/doi.org\/10.1016\/j.neucom.2016.01.015","journal-title":"Neuro-computing"},{"issue":"1","key":"12152_CR19","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1109\/TNN.2005.863458","volume":"17","author":"F Abdollahi","year":"2006","unstructured":"Abdollahi F et al (2006) A stable neural networks-based observer with application to flexible-joint manipulators. IEEE Trans Neural Networks 17(1):118\u2013129. https:\/\/doi.org\/10.1109\/TNN.2005.863458","journal-title":"IEEE Trans Neural Networks"},{"issue":"4","key":"12152_CR20","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1109\/TNN.2010.2041068","volume":"21","author":"M Gonzalez-Olvera","year":"2010","unstructured":"Gonzalez-Olvera M (2010) Black-box identification of a class of nonlinear systems by a neurofuzzy network. IEEE Trans Neural Netw 21(4):672\u2013679. https:\/\/doi.org\/10.1109\/TNN.2010.2041068","journal-title":"IEEE Trans Neural Netw"},{"issue":"10","key":"12152_CR21","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1109\/TCSII.2008.2001962","volume":"55","author":"J Resendiz","year":"2008","unstructured":"Resendiz J et al (2008) Two-stage neural observer for mechanical systems. IEEE Trans Circuits Syst 55(10):1076\u20131080. https:\/\/doi.org\/10.1109\/TCSII.2008.2001962","journal-title":"IEEE Trans Circuits Syst"},{"key":"12152_CR22","doi-asserted-by":"publisher","unstructured":"Krishna P., Anuj M., (2023), \u201cDeep learning based source identification of environmental audio signals using optimized convolution neural networks\u201d, Applied Soft Computing, vol. 143. https:\/\/doi.org\/10.1016\/j.asoc.2023.110423","DOI":"10.1016\/j.asoc.2023.110423"},{"issue":"3","key":"12152_CR23","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1007\/s00500-021-06093-6","volume":"27","author":"K Erdin\u00e7","year":"2021","unstructured":"Erdin\u00e7 K et al (2021) A new hybrid neural network classifier based on adaptive neuron and multiplicative neuron. Soft Comput 27(3):1797\u20131808. https:\/\/doi.org\/10.1007\/s00500-021-06093-6","journal-title":"Soft Comput"},{"key":"12152_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110550","author":"L Jiajie","year":"2023","unstructured":"Jiajie L, Lin X, Penglin C, Xiaopeng L (2023) A new class of robust and predefined-time consensus protocol based on noise-tolerant ZNN models. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2023.110550","journal-title":"Appl Soft Comput"},{"key":"12152_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110522","author":"L Zimeng","year":"2023","unstructured":"Zimeng L, Alexander O, Travis D (2023) Online evolutionary neural architecture search for multivariate non-stationary time series forecasting. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2023.110522","journal-title":"Appl Soft Comput"},{"issue":"8","key":"12152_CR26","doi-asserted-by":"publisher","first-page":"3473","DOI":"10.1002\/acs.3885","volume":"38","author":"Z Peng","year":"2024","unstructured":"Peng Z, Song X, Song S, Stojanovic V (2024) Spatiotemporal fault estimation for switched nonlinear reaction-diffusion systems via adaptive iterative learning. Int J Adapt Control Signal Process 38(8):3473\u20133483. https:\/\/doi.org\/10.1002\/acs.3885","journal-title":"Int J Adapt Control Signal Process"},{"key":"12152_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.oceaneng.2023.114647","volume":"281","author":"PNN Thanh","year":"2023","unstructured":"Thanh PNN, Anh HPH (2023) Neural network-based depth and horizontal control for autonomous underwater vehicles with prescribed performance. Ocean Eng 281:1\u201314. https:\/\/doi.org\/10.1016\/j.oceaneng.2023.114647","journal-title":"Ocean Eng"},{"issue":"108","key":"12152_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s40435-025-01612-x","volume":"13","author":"S Mobayen","year":"2025","unstructured":"Mobayen S et al (2025) Robust UAV tracker under actuator faults and external disturbances using a barrier function-based prescribed performance sliding mode control approach. Ocean Eng 13(108):01612. https:\/\/doi.org\/10.1007\/s40435-025-01612-x","journal-title":"Ocean Eng"},{"key":"12152_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2025.122111","volume":"711","author":"Y Xia","year":"2025","unstructured":"Xia Y et al (2025) Non-fragile fuzzy control of input-saturated systems with global prescribed performance via an error-triggered mechanism. Inf Sci 711:122111. https:\/\/doi.org\/10.1016\/j.ins.2025.122111","journal-title":"Inf Sci"},{"issue":"8","key":"12152_CR30","doi-asserted-by":"publisher","DOI":"10.3390\/sym14081511","volume":"14","author":"AB Colak","year":"2022","unstructured":"Colak AB et al (2022) Do artificial neural networks always provide high prediction performance? An experimental study on the insufficiency of artificial neural networks in capacitance prediction of the 6H-SiC\/MEH-PPV\/Al diode. Symmetry 14(8):1511. https:\/\/doi.org\/10.3390\/sym14081511","journal-title":"Symmetry"},{"issue":"3","key":"12152_CR31","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1615\/HeatTransRes.2021041018","volume":"53","author":"A Shafiq","year":"2022","unstructured":"Shafiq A et al (2022) Optimization of Darcy-Forchheimer squeezing flow in nonlinear stratified fluid under convective conditions with artificial neural network. Heat Transfer Res 53(3):67\u201389. https:\/\/doi.org\/10.1615\/HeatTransRes.2021041018","journal-title":"Heat Transfer Res"},{"issue":"6","key":"12152_CR32","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1007\/s40313-013-0082-6","volume":"24","author":"J Vargas","year":"2013","unstructured":"Vargas J et al (2013) On-line neuro identification of uncertain systems based on scaling and explicit feedback. Control Autom Electr Syst 24(6):753\u2013763. https:\/\/doi.org\/10.1007\/s40313-013-0082-6","journal-title":"Control Autom Electr Syst"},{"issue":"10","key":"12152_CR33","doi-asserted-by":"publisher","first-page":"7062","DOI":"10.1016\/j.ijleo.2016.05.010","volume":"127","author":"A Akgul","year":"2016","unstructured":"Akgul A, Hussain S, Pehlivan I (2016) A new three-dimensional chaotic system, its dynamical analysis and electronic circuit applications. Optik - Int J Light Electron Optics 127(10):7062\u20137071. https:\/\/doi.org\/10.1016\/j.ijleo.2016.05.010","journal-title":"Optik - Int J Light Electron Optics"},{"issue":"3","key":"12152_CR34","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.1007\/s11071-011-0137-9","volume":"67","author":"H Yu","year":"2012","unstructured":"Yu H, Cai G, Li Y (2012) Dynamic analysis and control of a new hyper-chaotic finance system. Nonlinear Dyn 67(3):2171\u20132182. https:\/\/doi.org\/10.1007\/s11071-011-0137-9","journal-title":"Nonlinear Dyn"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12152-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-12152-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12152-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T04:55:06Z","timestamp":1783054506000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-12152-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,29]]},"references-count":34,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["12152"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-12152-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,29]]},"assertion":[{"value":"8 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare that his agreement for publication this manuscript in case it is accepted.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that this research is original and ensures Ethics approval.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"439"}}