{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:46:06Z","timestamp":1776206766168,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T00:00:00Z","timestamp":1615075200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T00:00:00Z","timestamp":1615075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s11063-021-10466-1","type":"journal-article","created":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T05:38:17Z","timestamp":1615095497000},"page":"1579-1596","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["New Results for Prediction of Chaotic Systems Using Deep Recurrent Neural Networks"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6242-8262","authenticated-orcid":false,"given":"Jos\u00e9 de Jes\u00fas","family":"Serrano-P\u00e9rez","sequence":"first","affiliation":[]},{"given":"Guillermo","family":"Fern\u00e1ndez-Anaya","sequence":"additional","affiliation":[]},{"given":"Salvador","family":"Carrillo-Moreno","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,7]]},"reference":[{"issue":"24","key":"10466_CR1","doi-asserted-by":"publisher","first-page":"8317","DOI":"10.1007\/s00500-017-2779-0","volume":"22","author":"MA Ahandani","year":"2018","unstructured":"Ahandani MA, Ghiasi AR, Kharrati H (2018) Parameter identification of chaotic systems using a shuffled backtracking search optimization algorithm. Soft Comput 22(24):8317\u20138339. https:\/\/doi.org\/10.1007\/s00500-017-2779-0","journal-title":"Soft Comput"},{"key":"10466_CR2","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.ymssp.2017.06.001","volume":"99","author":"RB Alstrom","year":"2018","unstructured":"Alstrom RB, Moreau S, Marzocca P, Bollt E (2018) Nonlinear characterization of a Rossler system under periodic closed-loop control via time-frequency and bispectral analysis. Mech Syst Signal Process 99:567\u2013585. https:\/\/doi.org\/10.1016\/j.ymssp.2017.06.001","journal-title":"Mech Syst Signal Process"},{"key":"10466_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13132-0","volume-title":"Chaos modeling and control systems design","author":"A Azar","year":"2015","unstructured":"Azar A (2015) Chaos modeling and control systems design. Springer, Cham"},{"key":"10466_CR4","unstructured":"Bildirici M, Son\u00fcst\u00fcn B (2019) Chaos and exchange rates. In: Economic issues: global and local perspectives. Glasstree Academic Publishing, pp 70\u201376. https:\/\/www.cambridgeint.uk\/BFT"},{"issue":"2231","key":"10466_CR5","doi-asserted-by":"publisher","first-page":"20190351","DOI":"10.1098\/rspa.2019.0351","volume":"475","author":"MA Bucci","year":"2019","unstructured":"Bucci MA, Semeraro O, Allauzen A, Wisniewski G, Cordier L, Mathelin L (2019) Control of chaotic systems by deep reinforcement learning. Proc R Soc A Math Phys Eng Sci 475(2231):20190351. https:\/\/doi.org\/10.1098\/rspa.2019.0351","journal-title":"Proc R Soc A Math Phys Eng Sci"},{"issue":"1","key":"10466_CR6","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s11071-016-3269-0","volume":"88","author":"A Buscarino","year":"2017","unstructured":"Buscarino A, Frasca M, Branciforte M, Fortuna L, Sprott JC (2017) Synchronization of two R\u00f6ssler systems with switching coupling. Nonlinear Dyn 88(1):673\u2013683. https:\/\/doi.org\/10.1007\/s11071-016-3269-0","journal-title":"Nonlinear Dyn"},{"issue":"1","key":"10466_CR7","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s00521-017-2993-9","volume":"31","author":"X Chai","year":"2019","unstructured":"Chai X, Gan Z, Yuan K, Chen Y, Liu X (2019) A novel image encryption scheme based on DNA sequence operations and chaotic systems. Neural Comput Appl 31(1):219\u2013237. https:\/\/doi.org\/10.1007\/s00521-017-2993-9","journal-title":"Neural Comput Appl"},{"key":"10466_CR8","doi-asserted-by":"crossref","unstructured":"Chattopadhyay A, Hassanzadeh P, Subramanian D (2019) Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: reservoir computing, ANN, and RNN-LSTM, pp 1\u201321. arXiv:1906.08829","DOI":"10.31223\/OSF.IO\/FBXNS"},{"key":"10466_CR9","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.enbuild.2018.03.066","volume":"169","author":"Y Chen","year":"2018","unstructured":"Chen Y, Tan H, Berardi U (2018) A data-driven approach for building energy benchmarking using the Lorenz curve. Energy Build 169:319\u2013331. https:\/\/doi.org\/10.1016\/j.enbuild.2018.03.066","journal-title":"Energy Build"},{"issue":"9","key":"10466_CR10","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/TCSI.2016.2573283","volume":"63","author":"Z Chen","year":"2016","unstructured":"Chen Z, Yuan X, Yuan Y, Iu HHC, Fernando T (2016) Parameter identification of chaotic and hyper-chaotic systems using synchronization-based parameter observer. IEEE Trans Circuits Syst I Regul Pap 63(9):1464\u20131475. https:\/\/doi.org\/10.1109\/TCSI.2016.2573283","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"key":"10466_CR11","doi-asserted-by":"publisher","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In: EMNLP 2014\u20142014 Conference on empirical methods in natural language processing, proceedings of the conference, pp 1724\u20131734. https:\/\/doi.org\/10.3115\/v1\/d14-1179","DOI":"10.3115\/v1\/d14-1179"},{"issue":"02","key":"10466_CR12","doi-asserted-by":"publisher","first-page":"1650038","DOI":"10.1142\/S0218127416500383","volume":"26","author":"M-F Danca","year":"2016","unstructured":"Danca M-F, Feckan M, Kuznetsov N, Chen G (2016) Looking more closely at the Rabinovich\u2013Fabrikant system. Int J Bifurc Chaos 26(02):1650038. https:\/\/doi.org\/10.1142\/S0218127416500383. arXiv:1509.09206","journal-title":"Int J Bifurc Chaos"},{"issue":"1","key":"10466_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S0218127419300015","volume":"29","author":"MF Danca","year":"2019","unstructured":"Danca MF, Bourke P, Kuznetsov N (2019) Graphical structure of attraction basins of hidden chaotic attractors: the Rabinovich\u2013Fabrikant system. Int J Bifurc Chaos 29(1):1\u201313. https:\/\/doi.org\/10.1142\/S0218127419300015","journal-title":"Int J Bifurc Chaos"},{"key":"10466_CR14","volume-title":"A first course in chaotic dynamical systems? Theory and experiment","author":"R Devaney","year":"1992","unstructured":"Devaney R (1992) A first course in chaotic dynamical systems? Theory and experiment. Addison-Wesley, Reading"},{"key":"10466_CR15","doi-asserted-by":"publisher","first-page":"132495","DOI":"10.1016\/j.physd.2020.132495","volume":"408","author":"P Dubois","year":"2020","unstructured":"Dubois P, Gomez T, Planckaert L, Perret L (2020) Data-driven predictions of the Lorenz system. Physica D 408:132495. https:\/\/doi.org\/10.1016\/j.physd.2020.132495","journal-title":"Physica D"},{"key":"10466_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-60945-z","author":"A Eilersen","year":"2020","unstructured":"Eilersen A, Jensen MH, Sneppen K (2020) Chaos in disease outbreaks among prey. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-020-60945-z","journal-title":"Sci Rep"},{"issue":"1","key":"10466_CR17","first-page":"38","volume":"5","author":"J Pamina","year":"2019","unstructured":"Pamina J, Raja JB (2019) Survey on deep learning algorithms. Int J Emerg Technol Innov Eng 5(1):38\u201343","journal-title":"Int J Emerg Technol Innov Eng"},{"issue":"1","key":"10466_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1103\/physrevresearch.2.012080","volume":"2","author":"H Fan","year":"2020","unstructured":"Fan H, Jiang J, Zhang C, Wang X, Lai YC (2020) Long-term prediction of chaotic systems with machine learning. Phys Rev Res 2(1):1\u20136. https:\/\/doi.org\/10.1103\/physrevresearch.2.012080","journal-title":"Phys Rev Res"},{"issue":"8","key":"10466_CR19","doi-asserted-by":"publisher","first-page":"4574","DOI":"10.1016\/j.jfranklin.2020.01.050","volume":"357","author":"J Fei","year":"2020","unstructured":"Fei J, Wang H (2020) Recurrent neural network fractional-order sliding mode control of dynamic systems. J Frankl Inst 357(8):4574\u20134591. https:\/\/doi.org\/10.1016\/j.jfranklin.2020.01.050","journal-title":"J Frankl Inst"},{"key":"10466_CR20","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I (2016) Deep learning. The MIT Press, Cambridge"},{"key":"10466_CR21","doi-asserted-by":"publisher","first-page":"112842","DOI":"10.1016\/j.eswa.2019.112842","volume":"138","author":"Z Hajiabotorabi","year":"2019","unstructured":"Hajiabotorabi Z, Kazemi A, Samavati FF, Maalek Ghaini FM (2019) Improving DWT-RNN model via B-spline wavelet multiresolution to forecast a high-frequency time series. Expert Syst Appl 138:112842. https:\/\/doi.org\/10.1016\/j.eswa.2019.112842","journal-title":"Expert Syst Appl"},{"key":"10466_CR22","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780198507239.001.0001","volume-title":"Chaos and nonlinear dynamics? An introduction for scientists and engineers","author":"R Hilborn","year":"2000","unstructured":"Hilborn R (2000) Chaos and nonlinear dynamics? An introduction for scientists and engineers. Oxford University Press, Oxford"},{"issue":"8","key":"10466_CR23","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735. arXiv:1406.1078","journal-title":"Neural Comput"},{"issue":"6","key":"10466_CR24","doi-asserted-by":"publisher","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(6):114\u2013119. https:\/\/doi.org\/10.1109\/MCOM.2019.1800155","journal-title":"IEEE Commun Mag"},{"issue":"9\u201310","key":"10466_CR25","doi-asserted-by":"publisher","first-page":"6649","DOI":"10.1007\/s11042-019-08393-4","volume":"79","author":"A Javeed","year":"2020","unstructured":"Javeed A, Shah T (2020) Design of an S-box using Rabinovich\u2013Fabrikant system of differential equations perceiving third order nonlinearity. Multimed Tools Appl 79(9\u201310):6649\u20136660. https:\/\/doi.org\/10.1007\/s11042-019-08393-4","journal-title":"Multimed Tools Appl"},{"key":"10466_CR26","series-title":"Materials and engineering mechanics","volume-title":"Mechanical engineers handbook","author":"M Kutz","year":"2015","unstructured":"Kutz M (2015) Mechanical engineers handbook. Materials and engineering mechanics. Wiley, Hoboken"},{"key":"10466_CR27","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.neucom.2018.08.090","volume":"346","author":"HH Lian","year":"2019","unstructured":"Lian HH, Xiao SP, Wang Z, Zhang XH, Xiao HQ (2019) Further results on sampled-data synchronization control for chaotic neural networks with actuator saturation. Neurocomputing 346:30\u201337. https:\/\/doi.org\/10.1016\/j.neucom.2018.08.090","journal-title":"Neurocomputing"},{"key":"10466_CR28","doi-asserted-by":"publisher","first-page":"26102","DOI":"10.1109\/ACCESS.2019.2900371","volume":"7","author":"F Liu","year":"2019","unstructured":"Liu F, Cai M, Wang L, Lu Y (2019) An ensemble model based on adaptive noise reducer and over-fitting prevention LSTM for multivariate time series forecasting. IEEE Access 7:26102\u201326115. https:\/\/doi.org\/10.1109\/ACCESS.2019.2900371","journal-title":"IEEE Access"},{"key":"10466_CR29","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1201\/9780203734636-38","volume-title":"Deterministic nonperiodic flow","author":"E Lorenz","year":"2017","unstructured":"Lorenz E (2017) Deterministic nonperiodic flow. Universality in Chaos, CRC Press, Boca Raton, pp 367\u2013378. https:\/\/doi.org\/10.1201\/9780203734636-38"},{"key":"10466_CR30","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.ref.2020.05.002","volume":"34","author":"S Mandal","year":"2020","unstructured":"Mandal S, Mandal KK (2020) Optimal energy management of microgrids under environmental constraints using chaos enhanced differential evolution. Renew Energy Focus 34:129\u2013141. https:\/\/doi.org\/10.1016\/j.ref.2020.05.002","journal-title":"Renew Energy Focus"},{"issue":"11","key":"10466_CR31","doi-asserted-by":"publisher","first-page":"3370","DOI":"10.1109\/TNNLS.2019.2891257","volume":"30","author":"N Mohajerin","year":"2019","unstructured":"Mohajerin N, Waslander SL (2019) Multistep prediction of dynamic systems with recurrent neural networks. IEEE Trans Neural Netw Learn Syst 30(11):3370\u20133383. https:\/\/doi.org\/10.1109\/TNNLS.2019.2891257","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10466_CR32","doi-asserted-by":"publisher","unstructured":"Mohamed ST, B HME, Hassanien AE (2020) The international conference on advanced machine learning technologies and applications (AMLTA2019). In: Advances in intelligent systems and computing, vol 921. Springer International Publishing, Cham. https:\/\/doi.org\/10.1007\/978-3-030-14118-9. https:\/\/doi.org\/10.1007\/978-3-030-14118-9_74","DOI":"10.1007\/978-3-030-14118-9 10.1007\/978-3-030-14118-9_74"},{"key":"10466_CR33","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.mfglet.2018.10.002","volume":"18","author":"M Mozaffar","year":"2018","unstructured":"Mozaffar M, Paul A, Al-Bahrani R, Wolff S, Choudhary A, Agrawal A, Ehmann K, Cao J (2018) Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manuf Lett 18:35\u201339. https:\/\/doi.org\/10.1016\/j.mfglet.2018.10.002","journal-title":"Manuf Lett"},{"issue":"4","key":"10466_CR34","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s12591-016-0278-x","volume":"27","author":"A Ouannas","year":"2019","unstructured":"Ouannas A, Odibat Z, Shawagfeh N (2019) A new Q\u2013S synchronization results for discrete chaotic systems. Differ Equ Dyn Syst 27(4):413\u2013422. https:\/\/doi.org\/10.1007\/s12591-016-0278-x","journal-title":"Differ Equ Dyn Syst"},{"issue":"8","key":"10466_CR35","doi-asserted-by":"publisher","first-page":"3317","DOI":"10.1007\/s00521-017-3287-y","volume":"31","author":"F \u00d6zkaynak","year":"2019","unstructured":"\u00d6zkaynak F (2019) Construction of robust substitution boxes based on chaotic systems. Neural Comput Appl 31(8):3317\u20133326. https:\/\/doi.org\/10.1007\/s00521-017-3287-y","journal-title":"Neural Comput Appl"},{"key":"10466_CR36","doi-asserted-by":"publisher","first-page":"48361","DOI":"10.1109\/ACCESS.2020.2979324","volume":"8","author":"CS Pappu","year":"2020","unstructured":"Pappu CS, Carroll TL, Flores BC (2020) Simultaneous radar-communication systems using controlled chaos-based frequency modulated waveforms. IEEE Access 8:48361\u201348375. https:\/\/doi.org\/10.1109\/ACCESS.2020.2979324","journal-title":"IEEE Access"},{"key":"10466_CR37","unstructured":"Pascanu R, Mikolov T, Bengio Y (2012) On the difficulty of training recurrent neural networks. In: 30th International conference on machine learning, ICML 2013 (PART 3), pp 2347\u20132355. arXiv:1211.5063"},{"key":"10466_CR38","doi-asserted-by":"publisher","unstructured":"Poznyak A, Sanchez E, Perez J, Yu W (1997) Robust adaptive nonlinear system identification and trajectory tracking by dynamic neural networks. In: Proceedings of the 1997 American control conference (Cat. No. 97CH36041), vol 1. IEEE, pp 242\u2013246. https:\/\/doi.org\/10.1109\/ACC.1997.611794","DOI":"10.1109\/ACC.1997.611794"},{"issue":"4","key":"10466_CR39","first-page":"311","volume":"50","author":"M Rabinovich","year":"1979","unstructured":"Rabinovich M, Fabrikant A (1979) Stochastic self-modulation of waves in nonequilibrium media. Sov J Exp Theor Phys 50(4):311","journal-title":"Sov J Exp Theor Phys"},{"key":"10466_CR40","unstructured":"Raissi M, Perdikaris P, Karniadakis GE (2018) Multistep neural networks for data-driven discovery of nonlinear dynamical systems, pp 1\u201319. arXiv:1801.01236"},{"issue":"5","key":"10466_CR41","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/0375-9601(76)90101-8","volume":"57","author":"OE R\u00f6ssler","year":"1976","unstructured":"R\u00f6ssler OE (1976) An equation for continuous chaos. Phys Lett A 57(5):397\u2013398. https:\/\/doi.org\/10.1016\/0375-9601(76)90101-8","journal-title":"Phys Lett A"},{"key":"10466_CR42","volume-title":"Neural networks for applied sciences and engineering? From fundamentals to complex pattern recognition","author":"S Samarasinghe","year":"2007","unstructured":"Samarasinghe S (2007) Neural networks for applied sciences and engineering? From fundamentals to complex pattern recognition. Auerbach, Boca Raton"},{"key":"10466_CR43","volume-title":"Encyclopedia of nonlinear science","author":"A Scott","year":"2005","unstructured":"Scott A (2005) Encyclopedia of nonlinear science. Routledge, New York"},{"issue":"5","key":"10466_CR44","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1007\/s00034-018-0967-5","volume":"38","author":"Y Shekofteh","year":"2019","unstructured":"Shekofteh Y, Jafari S, Rajagopal K, Pham VT (2019) Parameter identification of chaotic systems using a modified cost function including static and dynamic information of attractors in the state space. Circuits Syst Signal Process 38(5):2039\u20132054. https:\/\/doi.org\/10.1007\/s00034-018-0967-5","journal-title":"Circuits Syst Signal Process"},{"issue":"8\u20139","key":"10466_CR45","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","volume":"108","author":"SY Shih","year":"2019","unstructured":"Shih SY, Sun FK, Lee H (2019) Temporal pattern attention for multivariate time series forecasting. Mach Learn 108(8\u20139):1421\u20131441. https:\/\/doi.org\/10.1007\/s10994-019-05815-0","journal-title":"Mach Learn"},{"key":"10466_CR46","doi-asserted-by":"publisher","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","volume":"7","author":"A Shrestha","year":"2019","unstructured":"Shrestha A, Mahmood A (2019) Review of deep learning algorithms and architectures. IEEE Access 7:53040\u201353065. https:\/\/doi.org\/10.1109\/ACCESS.2019.2912200","journal-title":"IEEE Access"},{"key":"10466_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-73004-2","volume-title":"Introduction to deep learning? From logical calculus to artificial intelligence","author":"S Skansi","year":"2018","unstructured":"Skansi S (2018) Introduction to deep learning? From logical calculus to artificial intelligence. Springer, Cham"},{"key":"10466_CR48","volume-title":"Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering","author":"S Strogatz","year":"2015","unstructured":"Strogatz S (2015) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. Westview Press, a member of the Perseus Books Group, Boulder"},{"key":"10466_CR49","volume-title":"Nonlinear dynamics and chaos","author":"JMT Thompson","year":"2002","unstructured":"Thompson JMT (2002) Nonlinear dynamics and chaos. Wiley, New York"},{"issue":"4","key":"10466_CR50","doi-asserted-by":"publisher","first-page":"2903","DOI":"10.1007\/s11071-019-05127-x","volume":"98","author":"R Wang","year":"2019","unstructured":"Wang R, Kalnay E, Balachandran B (2019) Neural machine-based forecasting of chaotic dynamics. Nonlinear Dyn 98(4):2903\u20132917. https:\/\/doi.org\/10.1007\/s11071-019-05127-x","journal-title":"Nonlinear Dyn"},{"key":"10466_CR51","doi-asserted-by":"crossref","unstructured":"Weiss G, Goldberg Y, Yahav E (2018) On the practical computational power of finite precision rnns for language recognition","DOI":"10.18653\/v1\/P18-2117"},{"issue":"4","key":"10466_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1103\/PhysRevE.99.042203","volume":"99","author":"T Weng","year":"2019","unstructured":"Weng T, Yang H, Gu C, Zhang J, Small M (2019) Synchronization of chaotic systems and their machine-learning models. Phys Rev E 99(4):1\u20137. https:\/\/doi.org\/10.1103\/PhysRevE.99.042203","journal-title":"Phys Rev E"},{"key":"10466_CR53","doi-asserted-by":"publisher","unstructured":"Zhang L (2017) Artificial neural networks model design of Lorenz chaotic system for EEG pattern recognition and prediction. In: 2017 IEEE life sciences conference (LSC), Jan. IEEE, pp 39\u201342. https:\/\/doi.org\/10.1109\/LSC.2017.8268138","DOI":"10.1109\/LSC.2017.8268138"},{"key":"10466_CR54","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.epsr.2018.09.006","volume":"166","author":"C Zheng","year":"2018","unstructured":"Zheng C, Wang S, Liu Y, Liu C (2018) A novel RNN based load modelling method with measurement data in active distribution system. Electr Power Syst Res 166:112\u2013124. https:\/\/doi.org\/10.1016\/j.epsr.2018.09.006","journal-title":"Electr Power Syst Res"},{"key":"10466_CR55","doi-asserted-by":"publisher","first-page":"20514","DOI":"10.1109\/ACCESS.2020.2968106","volume":"8","author":"L Zhuang","year":"2020","unstructured":"Zhuang L, Cao L, Wu Y, Zhong Y, Zhangzhong L, Zheng W, Wang L (2020) Parameter estimation of Lorenz chaotic system based on a hybrid Jaya\u2013Powell algorithm. IEEE Access 8:20514\u201320522. https:\/\/doi.org\/10.1109\/ACCESS.2020.2968106","journal-title":"IEEE Access"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10466-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10466-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10466-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T13:27:53Z","timestamp":1618320473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10466-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,7]]},"references-count":55,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["10466"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10466-1","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,7]]},"assertion":[{"value":"11 February 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2021","order":2,"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 of interest"}}]}}