{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T01:20:13Z","timestamp":1769908813090,"version":"3.49.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673154"],"award-info":[{"award-number":["61673154"]}],"id":[{"id":"10.13039\/501100001809","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":[[2022,4]]},"DOI":"10.1007\/s00521-021-06028-0","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T15:03:30Z","timestamp":1619017410000},"page":"5081-5095","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Fault diagnosis and prognosis of steer-by-wire system based on finite state machine and extreme learning machine"],"prefix":"10.1007","volume":"34","author":[{"given":"Dun","family":"Lan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3703-9163","authenticated-orcid":false,"given":"Ming","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yunzhi","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhaowu","family":"Ping","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"6028_CR1","doi-asserted-by":"publisher","first-page":"9589","DOI":"10.1109\/TVT.2020.3003326","volume":"69","author":"J Zhang","year":"2020","unstructured":"Zhang J, Wang H, Zheng J et al (2020) Adaptive sliding mode-based lateral stability control of steer-by-wire vehicles with experimental validations. IEEE Trans Veh Technol 69:9589\u20139600","journal-title":"IEEE Trans Veh Technol"},{"key":"6028_CR2","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.arcontrol.2019.04.001","volume":"47","author":"C Huang","year":"2019","unstructured":"Huang C, Naghdy F, Du H, Huang H (2019) Fault tolerant steer-by-wire systems: an overview. Annu Rev Control 47:98\u2013111","journal-title":"Annu Rev Control"},{"key":"6028_CR3","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/TCST.2007.894650","volume":"15","author":"C Gadda","year":"2007","unstructured":"Gadda C, Laws S, Gerdes J (2007) Generating diagnostic residuals for steer-by-wire vehicles. IEEE Trans Control Syst Technol 15:529\u2013540","journal-title":"IEEE Trans Control Syst Technol"},{"key":"6028_CR4","doi-asserted-by":"publisher","first-page":"6826","DOI":"10.1109\/TII.2020.2976043","volume":"16","author":"T Xue","year":"2020","unstructured":"Xue T, Zhong M, Li L, Ding S (2020) An optimal data-driven approach to distribution independent fault detection. IEEE Trans Ind Inf 16:6826\u20136836","journal-title":"IEEE Trans Ind Inf"},{"key":"6028_CR5","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s00521-015-1850-y","volume":"27","author":"Z Zhao","year":"2016","unstructured":"Zhao Z, Xu Q, Jia M (2016) Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis. Neural Comput Appl 27:375\u2013385","journal-title":"Neural Comput Appl"},{"key":"6028_CR6","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1109\/TSMC.2017.2710143","volume":"49","author":"O Prakash","year":"2019","unstructured":"Prakash O, Samantaray A, Bhattacharyya R (2019) Model-based diagnosis of multiple faults in hybrid dynamical systems with dynamically updated parameters. IEEE Trans Syst Man Cybern-Syst 49:1053\u20131073","journal-title":"IEEE Trans Syst Man Cybern-Syst"},{"key":"6028_CR7","doi-asserted-by":"publisher","first-page":"5233","DOI":"10.1109\/TII.2018.2842255","volume":"14","author":"M Yu","year":"2018","unstructured":"Yu M, Xiao C, Jiang W et al (2018) Fault diagnosis for electromechanical system via extended analytical redundancy relations. IEEE Trans Ind Inf 14:5233\u20135244","journal-title":"IEEE Trans Ind Inf"},{"key":"6028_CR8","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1109\/TAC.2020.2982907","volume":"66","author":"W Han","year":"2021","unstructured":"Han W, Wang Z, Shen Y, Xu B (2021) Interval estimation for uncertain systems via polynomial chaos expansions. IEEE Trans Autom Control 66:468\u2013475","journal-title":"IEEE Trans Autom Control"},{"key":"6028_CR9","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1109\/TCYB.2018.2794972","volume":"49","author":"B Xu","year":"2019","unstructured":"Xu B, Shi Z, Sun F, He W (2019) Barrier Lyapunov function based learning control of hypersonic flight vehicle with AOA constraint and actuator faults. IEEE T Cybern 49:1047\u20131057","journal-title":"IEEE T Cybern"},{"key":"6028_CR10","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1109\/TCST.2008.2011648","volume":"17","author":"M Daigle","year":"2009","unstructured":"Daigle M, Koutsoukos X, Biswas G (2009) A qualitative event-based approach to continuous systems diagnosis. IEEE Trans Control Syst Technol 17:780\u2013793","journal-title":"IEEE Trans Control Syst Technol"},{"key":"6028_CR11","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1109\/TPWRS.2017.2688131","volume":"33","author":"J Zhao","year":"2018","unstructured":"Zhao J (2018) Dynamic state estimation with model uncertainties using H\u221e extended Kalman filter. IEEE Trans Power Syst 33:1099\u20131110","journal-title":"IEEE Trans Power Syst"},{"key":"6028_CR12","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TCST.2014.2317781","volume":"23","author":"M Partovibakhsh","year":"2015","unstructured":"Partovibakhsh M, Liu G (2015) An adaptive unscented Kalman filtering approach for online estimation of model parameters and state-of-charge of lithium-ion batteries for autonomous mobile robots. IEEE Trans Control Syst Technol 23:357\u2013363","journal-title":"IEEE Trans Control Syst Technol"},{"key":"6028_CR13","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/TIE.2016.2619322","volume":"64","author":"M Yu","year":"2017","unstructured":"Yu M, Xia H, He Y et al (2017) Scheduled health monitoring of hybrid systems with multiple distinct faults. IEEE Trans Ind Electron 64:1517\u20131528","journal-title":"IEEE Trans Ind Electron"},{"key":"6028_CR14","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1109\/TR.2019.2948705","volume":"69","author":"C Huang","year":"2020","unstructured":"Huang C, Yin X, Huang H, Li Y (2020) An enhanced deep learning-based fusion prognostic method for RUL prediction. IEEE Trans Reliab 69:1097\u20131109","journal-title":"IEEE Trans Reliab"},{"key":"6028_CR15","doi-asserted-by":"publisher","first-page":"8472","DOI":"10.1109\/JSEN.2018.2866708","volume":"18","author":"Q Hu","year":"2018","unstructured":"Hu Q, Qin A, Zhang Q et al (2018) Fault diagnosis based on weighted extreme learning machine with wavelet packet decomposition and KPCA. IEEE Sens J 18:8472\u20138848","journal-title":"IEEE Sens J"},{"key":"6028_CR16","doi-asserted-by":"publisher","first-page":"14507","DOI":"10.1007\/s00521-019-04446-9","volume":"32","author":"Y Hu","year":"2020","unstructured":"Hu Y, Wang H, Cao Z et al (2020) Extreme-learning-machine-based FNTSM control strategy for electronic throttle. Neural Comput Appl 32:14507\u201314518","journal-title":"Neural Comput Appl"},{"key":"6028_CR17","doi-asserted-by":"publisher","first-page":"14447","DOI":"10.1007\/s00521-019-04502-4","volume":"32","author":"J Zhang","year":"2019","unstructured":"Zhang J, Wang H, Cao Z et al (2019) Fast nonsingular terminal sliding mode control for permanent magnet linear motor via ELM. Neural Comput Appl 32:14447\u201314457","journal-title":"Neural Comput Appl"},{"key":"6028_CR18","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.compeleceng.2020.106756","volume":"86","author":"M Ye","year":"2020","unstructured":"Ye M, Wang H (2020) Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine. Comput Electr Eng 86:445\u2013452","journal-title":"Comput Electr Eng"},{"key":"6028_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ymssp.2019.106375","volume":"135","author":"Y Hu","year":"2020","unstructured":"Hu Y, Wang H (2020) Robust tracking control for vehicle electronic throttle using adaptive dynamic sliding mode and extended state observer. Mech Syst Signal Process 135:1\u201318","journal-title":"Mech Syst Signal Process"},{"key":"6028_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3036400","author":"J Zhang","year":"2020","unstructured":"Zhang J, Wang H, Ma M et al (2020) Active front steering-based electronic stability control for steer-by-wire vehicles via terminal sliding mode and extreme learning machine. IEEE Trans Veh Technol. https:\/\/doi.org\/10.1109\/TVT.2020.3036400","journal-title":"IEEE Trans Veh Technol"},{"key":"6028_CR21","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1109\/TIE.2008.2010171","volume":"56","author":"Y Yamaguchi","year":"2009","unstructured":"Yamaguchi Y, Murakami T (2009) Adaptive control for virtual steering characteristics on electric vehicle using steer-by-wire system. IEEE Trans Ind Electron 56:1585\u20131594","journal-title":"IEEE Trans Ind Electron"},{"key":"6028_CR22","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s00521-013-1367-1","volume":"24","author":"X Yang","year":"2013","unstructured":"Yang X, Deb S (2013) Cuckoo search: recent advances and applications. Neural Comput Appl 24:169\u2013174","journal-title":"Neural Comput Appl"},{"key":"6028_CR23","doi-asserted-by":"publisher","first-page":"106899","DOI":"10.1016\/j.ymssp.2020.106899","volume":"144","author":"Z Pan","year":"2020","unstructured":"Pan Z, Meng Z, Chen ZJ et al (2020) A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings. Mech Syst Signal Proc 144:106899\u2013106916","journal-title":"Mech Syst Signal Proc"},{"key":"6028_CR24","first-page":"1","volume":"20","author":"Z Zhao","year":"2013","unstructured":"Zhao Z, Feng X, Lin Y et al (2013) Improved rao-blackwellized particle filter by particle swarm optimization. J Appl Math 20:1\u20137","journal-title":"J Appl Math"},{"key":"6028_CR25","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/j.jclepro.2017.06.016","volume":"20","author":"W Sun","year":"2017","unstructured":"Sun W, Wang C, Zhang C (2017) Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization. J Clean Prod 20:1095\u20131101","journal-title":"J Clean Prod"},{"key":"6028_CR26","first-page":"1045","volume":"11","author":"G Karystinos","year":"2000","unstructured":"Karystinos G, Pados D (2000) On overfitting, generalization, and randomly expanded training sets. IEEE Trans Neural Netw Learn Syst 11:1045\u20131053","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6028_CR27","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1007\/s11063-018-9865-x","volume":"49","author":"H Yu","year":"2019","unstructured":"Yu H, Sun X, Wang J (2019) A dynamic ELM with balanced variance and bias for long-term online prediction. Neural Process Lett 49:1257\u20131271","journal-title":"Neural Process Lett"},{"key":"6028_CR28","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1109\/TNNLS.2014.2362555","volume":"26","author":"K Ning","year":"2015","unstructured":"Ning K, Liu M, Dong M et al (2015) Two efficient twin ELM methods with prediction interval. IEEE Trans Neural Netw Learn Syst 26:2058\u20132071","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6028_CR29","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1109\/TCSS.2020.2969484","volume":"7","author":"Y Xiao","year":"2020","unstructured":"Xiao Y, Li J, Zhu Y et al (2020) User behavior prediction of social hotspots based on multimessage interaction and neural network. IEEE Trans Comput Social Syst 7:536\u2013546","journal-title":"IEEE Trans Comput Social Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06028-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06028-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06028-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T05:04:21Z","timestamp":1647320661000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06028-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,21]]},"references-count":29,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6028"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06028-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,21]]},"assertion":[{"value":"25 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2021","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict interest"}}]}}