{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T20:44:56Z","timestamp":1768077896234,"version":"3.49.0"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T00:00:00Z","timestamp":1620432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T00:00:00Z","timestamp":1620432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ftirma Kurumu","doi-asserted-by":"publisher","award":["118E807"],"award-info":[{"award-number":["118E807"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10489-021-02416-0","type":"journal-article","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T06:05:44Z","timestamp":1620453944000},"page":"662-679","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep learning frameworks to learn prediction and simulation focused control system models"],"prefix":"10.1007","volume":"52","author":[{"given":"Turcan","family":"Tuna","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aykut","family":"Beke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9366-0240","authenticated-orcid":false,"given":"Tufan","family":"Kumbasar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"key":"2416_CR1","volume-title":"Deep learning","author":"A Courville","year":"2016","unstructured":"Courville A, Goodfellow I, Bengio Y (2016) Deep learning. MIT press, Cambridge"},{"issue":"7553","key":"2416_CR2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"2416_CR3","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"2416_CR4","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/TMECH.2017.2722479","volume":"23","author":"O Janssens","year":"2018","unstructured":"Janssens O, Van De Walle R, Loccufier M, Van Hoecke S (2018) Deep learning for infrared thermal image based machine health monitoring. IEEE\/ASME Trans Mechatron 23:151\u2013159","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"2416_CR5","doi-asserted-by":"crossref","unstructured":"Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: ICASSP, IEEE Int Conf Acoust, Speech and Signal Process \u2013 Proc :8599\u20138603","DOI":"10.1109\/ICASSP.2013.6639344"},{"key":"2416_CR6","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","volume":"7","author":"L Deng","year":"2013","unstructured":"Deng L, Yu D (2013) Deep learning: methods and applications. Found Trends in Signal Process 7:197\u2013387","journal-title":"Found Trends in Signal Process"},{"key":"2416_CR7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11\u201326","journal-title":"Neurocomputing"},{"key":"2416_CR8","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1109\/TMECH.2019.2928967","volume":"24","author":"J Wang","year":"2019","unstructured":"Wang J, Fu P, Zhang L, Gao RX, Zhao R (2019) Multilevel information fusion for induction motor fault diagnosis. IEEE\/ASME Trans Mechatron 24:2139\u20132150","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"2416_CR9","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/TMECH.2018.2874647","volume":"24","author":"D Kim","year":"2019","unstructured":"Kim D, Kwon J, Han S, Park YL, Jo S (2019) Deep full-body motion network for a soft wearable motion sensing suit. IEEE\/ASME Trans Mechatron 24:56\u201366","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"2416_CR10","doi-asserted-by":"crossref","first-page":"103550","DOI":"10.1016\/j.engappai.2020.103550","volume":"91","author":"M Terzi","year":"2020","unstructured":"Terzi M, Susto GA, Chaudhari P (2020) Directional adversarial training for cost sensitive deep learning classification applications. Eng Appl Artif Intell 91:103550","journal-title":"Eng Appl Artif Intell"},{"key":"2416_CR11","doi-asserted-by":"crossref","unstructured":"Masti D, Bemporad A (2019) Learning nonlinear state-space models using deep autoencoders. In: Proc IEEE Conf Decis Control :3862\u20133867","DOI":"10.1109\/CDC.2018.8619475"},{"key":"2416_CR12","doi-asserted-by":"crossref","unstructured":"Bansal S, Akametalu AK, Jiang FJ, Laine F, Tomlin CJ (2016) Learning quadrotor dynamics using neural network for flight control. In: 2016 IEEE 55th Conf Decis control, CDC :4653\u20134660","DOI":"10.1109\/CDC.2016.7798978"},{"key":"2416_CR13","doi-asserted-by":"crossref","unstructured":"Gensler A, Henze J, Sick B, Raabe N (2016) Deep Learning for solar power forecasting - An approach using AutoEncoder and LSTM Neural Networks. In: 2016 IEEE Int Conf Syst, Man, and Cybern :2858\u20132865","DOI":"10.1109\/SMC.2016.7844673"},{"key":"2416_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-07210-0","volume":"9","author":"B Lusch","year":"2018","unstructured":"Lusch B, Kutz JN, Brunton SL (2018) Deep learning for universal linear embeddings of nonlinear dynamics. Nat Commun 9:1\u201310","journal-title":"Nat Commun"},{"key":"2416_CR15","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neunet.2017.10.006","volume":"104","author":"J Qiao","year":"2018","unstructured":"Qiao J, Wang G, Li W, Li X (2018) A deep belief network with PLSR for nonlinear system modeling. Neural Netw 104:68\u201379","journal-title":"Neural Netw"},{"key":"2416_CR16","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.ins.2015.09.048","volume":"364","author":"E de la Rosa","year":"2016","unstructured":"de la Rosa E, Yu W (2016) Randomized algorithms for nonlinear system identification with deep learning modification. Inf Sci 364:197\u2013212","journal-title":"Inf Sci"},{"key":"2416_CR17","doi-asserted-by":"crossref","unstructured":"Qiu X, Zhang L, Ren Y, Suganthan P, Amaratunga G (2014) Ensemble deep learning for regression and time series forecasting. In: 2014 IEEE Sym Comput Int Ensemble Learn, Proc :1\u20136","DOI":"10.1109\/CIEL.2014.7015739"},{"key":"2416_CR18","doi-asserted-by":"crossref","unstructured":"Hirose N, Tajima R (2017) Modeling of rolling friction by recurrent neural network using LSTM. In: Proc - IEEE Int Conf Robot Autom :6471\u20136478","DOI":"10.1109\/ICRA.2017.7989764"},{"key":"2416_CR19","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","volume":"323","author":"A Sagheer","year":"2019","unstructured":"Sagheer A, Kotb M (2019) Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323:203\u2013213","journal-title":"Neurocomputing"},{"key":"2416_CR20","doi-asserted-by":"crossref","unstructured":"Liu C, Jin Z, Gu J, Qiu C (2017) Short-term load forecasting using a long short-term memory network. In: 2017 IEEE PES Innov smart grid Technol Conf Eur Proc :1\u20136","DOI":"10.1109\/ISGTEurope.2017.8260110"},{"key":"2416_CR21","first-page":"68","volume":"11","author":"Z Zhao","year":"2017","unstructured":"Zhao Z, Chen W, Wu X, Chen PCV, Liu J (2017) LSTM network: a deep learning approach for short-term traffic forecast. IET Image Process 11:68\u201375","journal-title":"IET Image Process"},{"key":"2416_CR22","unstructured":"Qu X, Kang X, Chao Z, Shuai J, Ma X (2016) Short-term prediction of wind power based on deep long short-term memory. In: Asia-Pacific Power and Energy Eng Conf :1148\u20131152"},{"key":"2416_CR23","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270:654\u2013669","journal-title":"Eur J Oper Res"},{"key":"2416_CR24","unstructured":"Hsu D (2017) Time series forecasting based on augmented long short-term memory. arXiv preprint arXiv: 170700666 [csNE]"},{"key":"2416_CR25","first-page":"1","volume":"34","author":"N Laptev","year":"2017","unstructured":"Laptev N, Yosinski J, Erran Li L et al (2017) Time-series extreme event forecasting with neural networks at Uber. Int Conf Mach Learn - Time Ser Workshop 34:1\u20135","journal-title":"Int Conf Mach Learn - Time Ser Workshop"},{"issue":"6","key":"2416_CR26","doi-asserted-by":"crossref","first-page":"172988141666336","DOI":"10.1177\/1729881416663369","volume":"13","author":"DNT How","year":"2016","unstructured":"How DNT, Loo CK, Sahari KSM (2016) Behavior recognition for humanoid robots using long short-term memory. Int J Adv Robot Syst 13(6):1729881416663369","journal-title":"Int J Adv Robot Syst"},{"key":"2416_CR27","doi-asserted-by":"crossref","first-page":"104785","DOI":"10.1016\/j.knosys.2019.05.028","volume":"181","author":"Y Li","year":"2019","unstructured":"Li Y, Zhu Z, Kong D, Han H, Zhao Y (2019) EA-LSTM: evolutionary attention-based LSTM for time series prediction. Knowl-Based Syst 181:104785","journal-title":"Knowl-Based Syst"},{"key":"2416_CR28","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","volume":"57","author":"Y Hua","year":"2019","unstructured":"Hua Y, Zhao Z, Li R, Chen X, Liu Z, Zhang H (2019) Deep learning with long short-term memory for time series prediction. IEEE Commun Mag 57:114\u2013119","journal-title":"IEEE Commun Mag"},{"key":"2416_CR29","doi-asserted-by":"crossref","unstructured":"Jin X, Yu X, Wang X, et al (2020) Prediction for time series with CNN and LSTM. In: Lect Notes Electrical Eng 582:631\u2013641","DOI":"10.1007\/978-981-15-0474-7_59"},{"key":"2416_CR30","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.neucom.2018.09.076","volume":"323","author":"A Elsheikh","year":"2019","unstructured":"Elsheikh A, Yacout S, Ouali MS (2019) Bidirectional handshaking LSTM for remaining useful life prediction. Neurocomputing 323:148\u2013156","journal-title":"Neurocomputing"},{"key":"2416_CR31","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1109\/TSMC.2017.2712184","volume":"49","author":"K George","year":"2019","unstructured":"George K, Mutalik P (2019) A multiple model approach to time-series prediction using an online sequential learning algorithm. IEEE Trans Syst Man Cybern Syst 49:976\u2013990","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"2416_CR32","doi-asserted-by":"crossref","unstructured":"Kuremoto T, Obayashi M, Kobayashi K, Hirata T, Mabu S (2014) Forecast chaotic time series data by DBNs. In: Proc - 2014 7th Int Congr image and signal process :1130\u20131135","DOI":"10.1109\/CISP.2014.7003950"},{"key":"2416_CR33","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2017.03.027","volume":"151","author":"M Qin","year":"2017","unstructured":"Qin M, Li Z, Du Z (2017) Red tide time series forecasting by combining ARIMA and deep belief network. Knowl-Based Syst 151:39\u201352","journal-title":"Knowl-Based Syst"},{"key":"2416_CR34","first-page":"1","volume":"2018","author":"JY Choi","year":"2018","unstructured":"Choi JY, Lee B (2018) Combining LSTM network ensemble via adaptive weighting for improved time series forecasting. Math Probl Eng 2018:1\u20138","journal-title":"Math Probl Eng"},{"key":"2416_CR35","doi-asserted-by":"crossref","unstructured":"Zarkias KS, Passalis N, Tsantekidis A, Tefas A (2019) Deep reinforcement learning for financial trading using Price trailing. In: Proc IEEE Int Conf Acoust, Speech and Signal Process :3067\u20133071","DOI":"10.1109\/ICASSP.2019.8683161"},{"key":"2416_CR36","first-page":"381","volume":"3","author":"CK Wee","year":"2019","unstructured":"Wee CK, Nayak R (2019) Adaptive load forecasting using reinforcement learning with database technology. J Inf Telecommun 3:381\u2013399","journal-title":"J Inf Telecommun"},{"key":"2416_CR37","doi-asserted-by":"crossref","unstructured":"Hirata T, Kuremoto T, Obayashi M, Mabu S, Kobayashi K (2016) Deep belief network using reinforcement learning and its applications to time series forecasting. In: Lect notes Comput Sci (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) :30\u201337","DOI":"10.1007\/978-3-319-46675-0_4"},{"key":"2416_CR38","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","volume":"42","author":"M L\u00e4ngkvist","year":"2014","unstructured":"L\u00e4ngkvist M, Karlsson L, Loutfi A (2014) A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn Lett 42:11\u201324","journal-title":"Pattern Recogn Lett"},{"key":"2416_CR39","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.neucom.2019.12.118","volume":"388","author":"S Du","year":"2020","unstructured":"Du S, Li T, Yang Y, Horng SJ (2020) Multivariate time series forecasting via attention-based encoder\u2013decoder framework. Neurocomputing 388:269\u2013279","journal-title":"Neurocomputing"},{"key":"2416_CR40","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TSTE.2015.2434387","volume":"6","author":"CY Zhang","year":"2015","unstructured":"Zhang CY, Chen CLP, Gan M, Chen L (2015) Predictive deep Boltzmann machine for multiperiod wind speed forecasting. IEEE Trans Sustain Energy 6:1416\u20131425","journal-title":"IEEE Trans Sustain Energy"},{"key":"2416_CR41","doi-asserted-by":"crossref","first-page":"e0180944","DOI":"10.1371\/journal.pone.0180944","volume":"12","author":"W Bao","year":"2017","unstructured":"Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS One 12:e0180944","journal-title":"PLoS One"},{"key":"2416_CR42","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neucom.2013.03.047","volume":"137","author":"T Kuremoto","year":"2014","unstructured":"Kuremoto T, Kimura S, Kobayashi K, Obayashi M (2014) Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137:47\u201356","journal-title":"Neurocomputing"},{"key":"2416_CR43","doi-asserted-by":"crossref","unstructured":"Lin T, Guo T, Aberer K (2017) Hybrid neural networks for learning the trend in time series. In: IJCAI Int Joint Conf Artif Intell :2273\u20132279","DOI":"10.24963\/ijcai.2017\/316"},{"key":"2416_CR44","doi-asserted-by":"crossref","unstructured":"Wang Y (2017) A new concept using LSTM neural networks for dynamic system identification. In: Proc Am Control Conf :5324\u20135329","DOI":"10.23919\/ACC.2017.7963782"},{"key":"2416_CR45","unstructured":"Ogunmolu O, Gu X, Jiang, S, Gans N (2016). Nonlinear systems identification using deep dynamic neural networks. arXiv preprint arXiv:1610.01439"},{"key":"2416_CR46","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.neucom.2019.12.135","volume":"416","author":"B Donon","year":"2020","unstructured":"Donon B, Donnot B, Guyon I, Liu Z, Marot A, Panciatici P, Schoenauer M (2020) LEAP nets for system identification and application to power systems. Neurocomputing. 416:316\u2013327. https:\/\/doi.org\/10.1016\/j.neucom.2019.12.135","journal-title":"Neurocomputing."},{"key":"2416_CR47","doi-asserted-by":"crossref","unstructured":"Kashima K (2016) Nonlinear model reduction by deep autoencoder of noise response data. In: 2016 IEEE 55th Conf Decis control :5750\u20135755","DOI":"10.1109\/CDC.2016.7799153"},{"key":"2416_CR48","doi-asserted-by":"crossref","unstructured":"Punjani A, Abbeel P (2015) Deep learning helicopter dynamics models. In: Proc - IEEE Int Conf Robot Automa :3223\u20133230","DOI":"10.1109\/ICRA.2015.7139643"},{"key":"2416_CR49","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.apor.2018.06.011","volume":"78","author":"J Woo","year":"2018","unstructured":"Woo J, Park J, Yu C, Kim N (2018) Dynamic model identification of unmanned surface vehicles using deep learning network. Appl Ocean Res 78:123\u2013133","journal-title":"Appl Ocean Res"},{"key":"2416_CR50","doi-asserted-by":"crossref","unstructured":"Genc S (2017) Parametric system identification using deep convolutional neural networks. In: Proc Int Joint Conf Neural Netw :2112\u20132119","DOI":"10.1109\/IJCNN.2017.7966110"},{"key":"2416_CR51","doi-asserted-by":"crossref","unstructured":"Kashihara K (2018) Nonlinear system identification based on convolutional neural networks for multiple drug interactions. In: Proc Annu Int Conf IEEE Eng Med Biol Soc :1\u20134","DOI":"10.1109\/EMBC.2018.8512316"},{"key":"2416_CR52","doi-asserted-by":"crossref","first-page":"04018125","DOI":"10.1061\/(ASCE)EM.1943-7889.0001556","volume":"145","author":"RT Wu","year":"2019","unstructured":"Wu RT, Jahanshahi MR (2019) Deep convolutional neural network for structural dynamic response estimation and system identification. J Eng Mech 145:04018125","journal-title":"J Eng Mech"},{"key":"2416_CR53","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/LGRS.2019.2922536","volume":"17","author":"W Zhu","year":"2020","unstructured":"Zhu W, Li X, Liu C, Xue F, Han Y (2020) An STFT-LSTM system for P-wave identification. IEEE Geosci Remote Sens Lett 17:519\u2013523","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"2416_CR54","doi-asserted-by":"crossref","first-page":"32714","DOI":"10.1109\/ACCESS.2019.2903124","volume":"7","author":"C Feng","year":"2019","unstructured":"Feng C, Chang L, Li C, Ding T, Mai Z (2019) Controller optimization approach using LSTM-based identification model for pumped-storage units. IEEE Access 7:32714\u201332727","journal-title":"IEEE Access"},{"key":"2416_CR55","doi-asserted-by":"crossref","unstructured":"Wang G, Qiao J, Bi J, Zhou M (2019) An Efficient Deep Belief Network with Fuzzy Learning for Nonlinear System Modeling. In: 2019 IEEE Int Conf Syst, Man and Cybern :3549\u20133554","DOI":"10.1109\/SMC.2019.8914608"},{"key":"2416_CR56","doi-asserted-by":"crossref","unstructured":"Yeung E, Kundu S, Hodas N (2019) Learning deep neural network representations for Koopman operators of nonlinear dynamical systems. In: 2019 am control Conf :4832\u20134839","DOI":"10.23919\/ACC.2019.8815339"},{"key":"2416_CR57","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jprocont.2017.06.010","volume":"60","author":"CLC Mattos","year":"2017","unstructured":"Mattos CLC, Dai Z, Damianou A, Barreto GA, Lawrence ND (2017) Deep recurrent Gaussian processes for outlier-robust system identification. J Process Control 60:82\u201394","journal-title":"J Process Control"},{"key":"2416_CR58","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ifacol.2018.07.326","volume":"51","author":"J Gonzalez","year":"2018","unstructured":"Gonzalez J, Yu W (2018) Non-linear system modeling using LSTM neural networks. IFAC-PapersOnLine 51:485\u2013489","journal-title":"IFAC-PapersOnLine"},{"key":"2416_CR59","doi-asserted-by":"crossref","first-page":"546","DOI":"10.3390\/pr7080546","volume":"7","author":"M Jiang","year":"2019","unstructured":"Jiang M, Jin Q (2019) Multivariable system identification method based on continuous action reinforcement learning automata. Processes 7:546","journal-title":"Processes"},{"key":"2416_CR60","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1007\/s13042-018-0847-0","volume":"10","author":"W Yu","year":"2019","unstructured":"Yu W, de la Rosa E (2019) Deep Boltzmann machine for nonlinear system modelling. Int J Mach Learn Cybern 10:1705\u20131716","journal-title":"Int J Mach Learn Cybern"},{"key":"2416_CR61","unstructured":"Zhou H, Ibrahim C, Pan W (2019). A sparse Bayesian deep learning approach for identification of cascaded tanks benchmark. arXiv preprint arXiv:1911.06847"},{"key":"2416_CR62","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28:2222\u20132232","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2416_CR63","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/10618600.2017.1366910","volume":"27","author":"AC Mestres","year":"2018","unstructured":"Mestres AC, Bochkina N, Mayer C (2018) Selection of the regularization parameter in graphical models using network characteristics. J Comput Graph Stat 27:323\u2013333","journal-title":"J Comput Graph Stat"},{"key":"2416_CR64","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.sysconle.2013.04.005","volume":"62","author":"Z Liu","year":"2013","unstructured":"Liu Z, Hansson A, Vandenberghe L (2013) Nuclear norm system identification with missing inputs and outputs. Syst Control Lett 62:605\u2013612","journal-title":"Syst Control Lett"},{"key":"2416_CR65","doi-asserted-by":"crossref","unstructured":"Hansson A, Liu Z, Vandenberghe L (2012) Subspace system identification via weighted nuclear norm optimization. In: Proceedings of the IEEE Conf Decis Control :3439\u20133444","DOI":"10.1109\/CDC.2012.6426980"},{"key":"2416_CR66","unstructured":"McNames Portland State University. http:\/\/web.cecs.pdx.edu\/~mcnames\/DataSets\/index.html. Accessed 28 June 2020"},{"key":"2416_CR67","doi-asserted-by":"crossref","unstructured":"Eyoh I, John R, De Maere G (2017) Time series forecasting with interval type-2 intuitionistic fuzzy logic systems. In: IEEE Int Conf Fuzzy Sys :1\u20136","DOI":"10.1109\/FUZZ-IEEE.2017.8015463"},{"key":"2416_CR68","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/TFUZZ.2010.2046904","volume":"18","author":"CF Juang","year":"2010","unstructured":"Juang CF, Huang RB, Cheng WY (2010) An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems. IEEE Trans Fuzzy Syst 18:686\u2013699","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"2416_CR69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TFUZZ.2003.817839","volume":"12","author":"JH Chiang","year":"2004","unstructured":"Chiang JH, Hao PY (2004) Support vector learning mechanism for fuzzy rule-based modeling: a new approach. IEEE Trans Fuzzy Syst 12:1\u201312","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"2416_CR70","unstructured":"Australian Energy Market Operator (2013). http:\/\/www.aemo.com.au. Accessed 28 June 2020"},{"key":"2416_CR71","unstructured":"Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. John Wiley & Sons, Hoboken,\u00a0New Jersey"},{"key":"2416_CR72","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.petrol.2013.03.004","volume":"106","author":"N Chithra Chakra","year":"2013","unstructured":"Chithra Chakra N, Song KY, Gupta MM, Saraf DN (2013) An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs). J Pet Sci Eng 106:18\u201333","journal-title":"J Pet Sci Eng"},{"key":"2416_CR73","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.ifacol.2017.08.071","volume":"50","author":"M Schoukens","year":"2017","unstructured":"Schoukens M, No\u00ebl JP (2017) Three benchmarks addressing open challenges in nonlinear system identification. IFAC-PapersOnLine 50:446\u2013451","journal-title":"IFAC-PapersOnLine"},{"key":"2416_CR74","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.automatica.2017.02.030","volume":"80","author":"A Svensson","year":"2017","unstructured":"Svensson A, Sch\u00f6n TB (2017) A flexible state\u2013space model for learning nonlinear dynamical systems. Automatica 80:189\u2013199","journal-title":"Automatica"},{"key":"2416_CR75","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.ymssp.2017.12.027","volume":"106","author":"P Mattsson","year":"2018","unstructured":"Mattsson P, Zachariah D, Stoica P (2018) Identification of cascade water tanks using a PWARX model. Mech Syst Signal Process 106:40\u201348","journal-title":"Mech Syst Signal Process"},{"key":"2416_CR76","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.ifacol.2017.08.088","volume":"50","author":"M Brunot","year":"2017","unstructured":"Brunot M, Janot A, Carrillo F (2017) Continuous-time nonlinear systems identification with output error method based on derivative-free optimisation. IFAC-PapersOnLine 50:464\u2013469","journal-title":"IFAC-PapersOnLine"},{"key":"2416_CR77","unstructured":"Schoukens M, Scheiwe FG (2016) Modeling nonlinear systems using a Volterra feedback model, workshop on nonlinear system identification benchmarks, Brussels, Belgium"},{"key":"2416_CR78","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.ifacol.2017.08.074","volume":"50","author":"R Relan","year":"2017","unstructured":"Relan R, Tiels K, Marconato A, Schoukens J (2017) An unstructured flexible nonlinear model for the cascaded water-tanks benchmark. IFAC-PapersOnLine 50:452\u2013457","journal-title":"IFAC-PapersOnLine"},{"key":"2416_CR79","unstructured":"De Moor BLR (1997) DaISy: Database for the Identification of Systems http:\/\/homes.esat.kuleuven.be\/~smc\/daisy\/. Accessed 28 June 2020"},{"key":"2416_CR80","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.neucom.2018.11.025","volume":"330","author":"X Hong","year":"2019","unstructured":"Hong X, Mitchell R, Di Fatta G (2019) Simplex basis function based sparse least squares support vector regression. Neurocomputing 330:394\u2013402","journal-title":"Neurocomputing"},{"key":"2416_CR81","unstructured":"MATLAB and System Identification Toolbox Release (2019a), The MathWorks, Inc., Natick, Massachusetts, United States"}],"updated-by":[{"DOI":"10.1007\/s10489-021-02538-5","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T00:00:00Z","timestamp":1621641600000}}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02416-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02416-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02416-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T06:37:26Z","timestamp":1642142246000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02416-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,8]]},"references-count":81,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["2416"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02416-0","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s10489-021-02538-5","asserted-by":"object"}]},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,8]]},"assertion":[{"value":"3 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2021","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s10489-021-02538-5","URL":"https:\/\/doi.org\/10.1007\/s10489-021-02538-5","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}