{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:25:06Z","timestamp":1772933106636,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"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":"crossref","award":["61890930-5"],"award-info":[{"award-number":["61890930-5"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61533002"],"award-info":[{"award-number":["61533002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61603009"],"award-info":[{"award-number":["61603009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Project","award":["2018YFC1900800-5"],"award-info":[{"award-number":["2018YFC1900800-5"]}]},{"name":"Beijing Natural Science Foundation under Grants","award":["4182007"],"award-info":[{"award-number":["4182007"]}]},{"name":"Beijing Municipal Education Commission Foundation","award":["KM201910005023"],"award-info":[{"award-number":["KM201910005023"]}]},{"name":"Major Science and Technology Program for Water Pollution Control and Treatment of China","award":["2018ZX07111005"],"award-info":[{"award-number":["2018ZX07111005"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s00521-020-05276-w","type":"journal-article","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T08:07:05Z","timestamp":1598861225000},"page":"5089-5109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A self-organizing recurrent fuzzy neural network based on multivariate time series analysis"],"prefix":"10.1007","volume":"33","author":[{"given":"Haixu","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1707-6074","authenticated-orcid":false,"given":"Junfei","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,31]]},"reference":[{"key":"5276_CR1","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.knosys.2018.04.014","volume":"152","author":"KV Shihabudheen","year":"2018","unstructured":"Shihabudheen KV, Pillai GN (2018) Recent advances in neuro-fuzzy system: a survey. Knowledge-Based Syst 152:136\u2013162","journal-title":"Knowledge-Based Syst"},{"key":"5276_CR2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.neucom.2015.02.092","volume":"174","author":"AD Alm\u00e1si","year":"2016","unstructured":"Alm\u00e1si AD, Wo\u017aniak S, Cristea V et al (2016) Review of advances in neural networks: neural design technology stack. Neurocomputing 174:31\u201341","journal-title":"Neurocomputing"},{"issue":"3","key":"5276_CR3","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1109\/TFUZZ.2017.2718497","volume":"26","author":"MM Ebadzadeh","year":"2018","unstructured":"Ebadzadeh MM, Salimi-Badr A (2018) IC-FNN: a novel fuzzy neural network with interpretable, intuitive, and correlated-contours fuzzy rules for function approximation. IEEE Trans Fuzzy Syst 26(3):1288\u20131302","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"5276_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/tfuzz.2019.2931870","author":"HG Han","year":"2019","unstructured":"Han HG, Wu XL, Liu Z, Qiao JF (2019) Data-knowledge-based fuzzy neural network for nonlinear system identification. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/tfuzz.2019.2931870","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"5276_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04372-w","author":"AA Khater","year":"2019","unstructured":"Khater AA, El-Nagar AM, El-Bardini M, El-Rabaie NM (2019) Online learning based on adaptive learning rate for a class of recurrent fuzzy neural network. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04372-w","journal-title":"Neural Comput Appl"},{"key":"5276_CR6","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.fss.2013.05.007","volume":"237","author":"GD Wu","year":"2014","unstructured":"Wu GD, Zhu ZW (2014) An enhanced discriminability recurrent fuzzy neural network for temporal classification problems. Fuzzy Sets Syst 237:47\u201362","journal-title":"Fuzzy Sets Syst"},{"key":"5276_CR7","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/1934575","author":"QD Zhu","year":"2019","unstructured":"Zhu QD, Han Y, Liu P et al (2019) Motion planning of autonomous mobile robot using recurrent fuzzy neural network trained by extended Kalman filter. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2019\/1934575","journal-title":"Comput Intell Neurosci"},{"key":"5276_CR8","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.asoc.2019.02.032","volume":"78","author":"C Luo","year":"2019","unstructured":"Luo C, Tan C, Wang X, Zheng Y (2019) An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction. Appl Soft Comput J 78:150\u2013163","journal-title":"Appl Soft Comput J"},{"key":"5276_CR9","doi-asserted-by":"crossref","first-page":"3881","DOI":"10.1007\/s10489-019-01439-y","volume":"49","author":"A Bencherif","year":"2019","unstructured":"Bencherif A, Chouireb F (2019) A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking. Appl Intell 49:3881\u20133893","journal-title":"Appl Intell"},{"key":"5276_CR10","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1080\/15325008.2017.1402395","volume":"45","author":"K Premkumar","year":"2017","unstructured":"Premkumar K, Manikandan BV, Kumar CA (2017) Antlion algorithm optimized fuzzy PID supervised on-line recurrent fuzzy neural network based controller for brushless DC motor. Electr Power Compon Syst 45:2304\u20132317","journal-title":"Electr Power Compon Syst"},{"key":"5276_CR11","doi-asserted-by":"crossref","first-page":"106106","DOI":"10.1016\/j.epsr.2019.106106","volume":"179","author":"Z Wen","year":"2020","unstructured":"Wen Z, Xie L, Fan Q, Feng H (2020) Long term electric load forecasting based on TS-type recurrent fuzzy neural network model. Electr Power Syst Res 179:106106","journal-title":"Electr Power Syst Res"},{"key":"5276_CR12","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1002\/asjc.1844","volume":"21","author":"JF Qiao","year":"2019","unstructured":"Qiao JF, Han GT, Han HG et al (2019) Decoupling control for wastewater treatment process based on recurrent fuzzy neural network. Asian J Control 21:1270\u20131280","journal-title":"Asian J Control"},{"key":"5276_CR13","doi-asserted-by":"crossref","first-page":"3847","DOI":"10.1016\/j.jfranklin.2013.03.014","volume":"351","author":"X Chen","year":"2014","unstructured":"Chen X, Xue AK, Peng DL, Guo Y (2014) Modeling of pH neutralization process using fuzzy recurrent neural network and DNA based NSGA-II. J Franklin Inst 351:3847\u20133864","journal-title":"J Franklin Inst"},{"key":"5276_CR14","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8014019","author":"Q Zhu","year":"2018","unstructured":"Zhu Q, Han Y, Cai C, Xiao Y (2018) Robust optimal navigation using nonlinear model predictive control method combined with recurrent fuzzy neural network. Math Probl Eng. https:\/\/doi.org\/10.1155\/2018\/8014019","journal-title":"Math Probl Eng"},{"key":"5276_CR15","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s11235-015-9968-x","volume":"63","author":"PA Mastorocostas","year":"2016","unstructured":"Mastorocostas PA, Hilas CS, Varsamis DN, Dova SC (2016) Telecommunications call volume forecasting with a block-diagonal recurrent fuzzy neural network. Telecommun Syst 63:15\u201325","journal-title":"Telecommun Syst"},{"key":"5276_CR16","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.gene.2012.06.042","volume":"506","author":"S Vineetha","year":"2012","unstructured":"Vineetha S, Chandra Shekara Bhat C, Idicula SM (2012) Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network. Gene 506:408\u2013416","journal-title":"Gene"},{"key":"5276_CR17","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1108\/IJICC-12-2016-0069","volume":"10","author":"JF Qiao","year":"2017","unstructured":"Qiao JF, Han GT, Han HG, Chai W (2017) Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network. Int J Intell Comput Cybern 10:94\u2013110","journal-title":"Int J Intell Comput Cybern"},{"key":"5276_CR18","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.procs.2017.11.283","volume":"120","author":"K Uyar","year":"2017","unstructured":"Uyar K, Ilhan A (2017) Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Procedia Comput Sci 120:588\u2013593","journal-title":"Procedia Comput Sci"},{"key":"5276_CR19","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cmpb.2016.02.009","volume":"128","author":"CH Lu","year":"2016","unstructured":"Lu CH, Wang WC, Tai CC, Chen TC (2016) Design of a heart rate controller for treadmill exercise using a recurrent fuzzy neural network. Comput Methods Programs Biomed 128:27\u201339","journal-title":"Comput Methods Programs Biomed"},{"key":"5276_CR20","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1109\/TPEL.2010.2046648","volume":"25","author":"CS Chen","year":"2010","unstructured":"Chen CS (2010) TSK-type self-organizing recurrent-neural-fuzzy control of linear microstepping motor drives. IEEE Trans Power Electron 25:2253\u20132265","journal-title":"IEEE Trans Power Electron"},{"key":"5276_CR21","first-page":"1","volume":"8","author":"JF Qiao","year":"2017","unstructured":"Qiao JF, Cai J, Han HG, Cai JX (2017) Predicting PM2.5 concentrations at a regional background station using second order self-organizing fuzzy neural network. Atmosphere (Basel) 8:1\u201317","journal-title":"Atmosphere (Basel)"},{"key":"5276_CR22","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.isatra.2018.10.014","volume":"84","author":"TY Zhao","year":"2019","unstructured":"Zhao TY, Li P, Cao JT (2019) Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network. ISA Trans 84:237\u2013246","journal-title":"ISA Trans"},{"key":"5276_CR23","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/TCYB.2017.2762521","volume":"49","author":"HG Han","year":"2019","unstructured":"Han HG, Zhang L, Wu XX, Qiao JF (2019) An efficient second-order algorithm for self-organizing fuzzy neural networks. IEEE Trans Cybern 49:14\u201326","journal-title":"IEEE Trans Cybern"},{"key":"5276_CR24","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.fss.2017.09.007","volume":"337","author":"F Sabahi","year":"2018","unstructured":"Sabahi F (2018) Introducing validity into self-organizing fuzzy neural network applied to impedance force control. Fuzzy Sets Syst 337:113\u2013127","journal-title":"Fuzzy Sets Syst"},{"key":"5276_CR25","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neucom.2019.01.045","volume":"337","author":"ZT Wen","year":"2019","unstructured":"Wen ZT, Xie LB, Feng HW, Tan Y (2019) Infrared flame detection based on a self-organizing TS-type fuzzy neural network. Neurocomputing 337:67\u201379","journal-title":"Neurocomputing"},{"key":"5276_CR26","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.neucom.2017.05.065","volume":"266","author":"HG Han","year":"2017","unstructured":"Han HG, Lin ZL, Qiao JF (2017) Modeling of nonlinear systems using the self-organizing fuzzy neural network with adaptive gradient algorithm. Neurocomputing 266:566\u2013578","journal-title":"Neurocomputing"},{"key":"5276_CR27","first-page":"261","volume":"18","author":"CF Juang","year":"2010","unstructured":"Juang CF, Da HC (2010) A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling. IEEE Trans Fuzzy Syst 18:261\u2013273","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"5276_CR28","first-page":"688","volume":"21","author":"RJ Wai","year":"2012","unstructured":"Wai RJ, Lin YW (2012) Adaptive moving-target tracking control of a vision-based mobile robot via a dynamic petri recurrent fuzzy neural network. IEEE Trans Fuzzy Syst 21(4):688\u2013701","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"5276_CR29","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/3516.974859","volume":"6","author":"FJ Lin","year":"2001","unstructured":"Lin FJ, Shyu KK, Wai RJ (2001) Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggle servomechanism. IEEE\/ASME Trans Mechatron 6(4):453\u2013466","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"5276_CR30","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.isatra.2013.08.012","volume":"53","author":"SI Han","year":"2014","unstructured":"Han SI, Lee JM (2014) Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems. ISA Trans 53:33\u201343","journal-title":"ISA Trans"},{"issue":"3\u20134","key":"5276_CR31","first-page":"501","volume":"62","author":"GZ Xu","year":"2011","unstructured":"Xu GZ, Song AG, Li HJ (2011) Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural network. J Intell Robot Syst 62(3\u20134):501\u2013525","journal-title":"J Intell Robot Syst"},{"key":"5276_CR32","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.neucom.2018.02.049","volume":"290","author":"HG Han","year":"2018","unstructured":"Han HG, Chen ZY, Liu HX, Qiao JF (2018) A self-organizing interval Type-2 fuzzy-neural-network for modeling nonlinear systems. Neurocomputing 290:196\u2013207","journal-title":"Neurocomputing"},{"key":"5276_CR33","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.neucom.2015.12.098","volume":"191","author":"A Mohammadzadeh","year":"2016","unstructured":"Mohammadzadeh A, Ghaemi S (2016) A modified sliding mode approach for synchronization of fractional-order chaotic\/hyperchaotic systems by using new self-structuring hierarchical type-2 fuzzy neural network. Neurocomputing 191:200\u2013213","journal-title":"Neurocomputing"},{"key":"5276_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fss.2018.12.008","volume":"375","author":"C Wang","year":"2019","unstructured":"Wang C, Agarwal RP, O\u2019Regan D (2019) Calculus of fuzzy vector-valued functions and almost periodic fuzzy vector-valued functions on time scales. Fuzzy Sets Syst 375:1\u201352","journal-title":"Fuzzy Sets Syst"},{"issue":"8","key":"5276_CR35","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1016\/j.cnsns.2013.12.038","volume":"19","author":"C Wang","year":"2014","unstructured":"Wang C (2014) Almost periodic solutions of impulsive BAM neural networks with variable delays on time scales. Commun Nonlinear Sci Numer Simul 19(8):2828\u20132842","journal-title":"Commun Nonlinear Sci Numer Simul"},{"key":"5276_CR36","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.nahs.2017.08.005","volume":"27","author":"R Sakthivel","year":"2018","unstructured":"Sakthivel R, Wang C, Santra S, Kaviarasan B (2018) Non-fragile reliable sampled-data controller for nonlinear switched time-varying systems. Nonlinear Anal Hybrid Syst 27:62\u201376","journal-title":"Nonlinear Anal Hybrid Syst"},{"issue":"3","key":"5276_CR37","doi-asserted-by":"crossref","first-page":"158","DOI":"10.4258\/hir.2010.16.3.158","volume":"16","author":"HJ Kam","year":"2010","unstructured":"Kam HJ, Sung JO, Park RW (2010) Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthc Inform Res 16(3):158\u2013165","journal-title":"Healthc Inform Res"},{"issue":"8","key":"5276_CR38","doi-asserted-by":"crossref","first-page":"3868","DOI":"10.1016\/j.eswa.2015.01.026","volume":"42","author":"TW Joo","year":"2015","unstructured":"Joo TW, Kim SB (2015) Time series forecasting based on wavelet filtering. Expert Syst Appl 42(8):3868\u20133874","journal-title":"Expert Syst Appl"},{"key":"5276_CR39","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.energy.2016.10.120","volume":"118","author":"W Sun","year":"2017","unstructured":"Sun W, Xu YF (2017) Research on China\u2019s energy supply and demand using an improved Grey\u2013Markov chain model based on wavelet transform. Energy 118:969\u2013984","journal-title":"Energy"},{"issue":"4","key":"5276_CR40","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/00207720903045783","volume":"41","author":"CJ Lin","year":"2010","unstructured":"Lin CJ, Lee CY (2010) Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation. Int J Syst Sci 41(4):381\u2013395","journal-title":"Int J Syst Sci"},{"issue":"2","key":"5276_CR41","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1109\/TNN.2006.871707","volume":"17","author":"P Lauret","year":"2006","unstructured":"Lauret P, Fock E, Mara TA (2006) A node pruning algorithm based on a Fourier amplitude sensitivity test method. IEEE Trans Neural Netw 17(2):273\u2013293","journal-title":"IEEE Trans Neural Netw"},{"key":"5276_CR42","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.neunet.2013.06.004","volume":"46","author":"N Vukovi\u0107","year":"2013","unstructured":"Vukovi\u0107 N, Miljkovi\u0107 Z (2013) A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation. Neural Netw 46:210\u2013226","journal-title":"Neural Netw"},{"key":"5276_CR43","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.neucom.2012.07.023","volume":"99","author":"HG Han","year":"2013","unstructured":"Han HG, Qiao JF (2013) A structure optimisation algorithm for feedforward neural network construction. Neurocomputing 99:347\u2013357","journal-title":"Neurocomputing"},{"key":"5276_CR44","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.neucom.2016.12.092","volume":"262","author":"JF Qiao","year":"2017","unstructured":"Qiao JF, Li SY, Han HG, Wang DH (2017) An improved algorithm for building self-organizing feedforward neural networks. Neurocomputing 262:28\u201340","journal-title":"Neurocomputing"},{"issue":"5","key":"5276_CR45","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1016\/j.eswa.2014.11.007","volume":"42","author":"T G\u00f3recki","year":"2015","unstructured":"G\u00f3recki T, \u0141uczak M (2015) Multivariate time series classification with parametric derivative dynamic time warping. Expert Syst Appl 42(5):2305\u20132312","journal-title":"Expert Syst Appl"},{"issue":"9","key":"5276_CR46","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","volume":"44","author":"YS Jeong","year":"2011","unstructured":"Jeong YS, Jeong MK, Omitaomu OA (2011) Weighted dynamic time warping for time series classification. Pattern Recognit 44(9):2231\u20132240","journal-title":"Pattern Recognit"},{"issue":"2","key":"5276_CR47","first-page":"159","volume":"11","author":"BF Vajargah","year":"2014","unstructured":"Vajargah BF, Gharehdaghi M (2014) Ergodicity of fuzzy Markov chains based on simulation using sequences. Int J Appl Math Comput Sci 11(2):159\u2013165","journal-title":"Int J Appl Math Comput Sci"},{"issue":"1","key":"5276_CR48","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/01431161.2013.870677","volume":"35","author":"GAOP Costa","year":"2014","unstructured":"Costa GAOP, Feitosa RQ (2014) A generalized fuzzy Markov chain-based model for classification of remote-sensing multitemporal images. Int J Remote Sens 35(1):341\u2013364","journal-title":"Int J Remote Sens"},{"issue":"3","key":"5276_CR49","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.compfluid.2009.09.013","volume":"39","author":"JEV Peter","year":"2010","unstructured":"Peter JEV, Dwight RP (2010) Numerical sensitivity analysis for aerodynamic optimization: a survey of approaches. Comput Fluids 39(3):373\u2013391","journal-title":"Comput Fluids"},{"issue":"7","key":"5276_CR50","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.ress.2007.04.002","volume":"93","author":"B Sudret","year":"2008","unstructured":"Sudret B (2008) Global sensitivity analysis using polynomial chaos expansions. Reliab Eng Syst Saf 93(7):964\u2013979","journal-title":"Reliab Eng Syst Saf"},{"issue":"2","key":"5276_CR51","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.ress.2008.06.016","volume":"94","author":"W Yu","year":"2009","unstructured":"Yu W, Harris TJ (2009) Parameter uncertainty effects on variance-based sensitivity analysis. Reliab Eng Syst Saf 94(2):596\u2013603","journal-title":"Reliab Eng Syst Saf"},{"issue":"6","key":"5276_CR52","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1109\/TFUZZ.2010.2070841","volume":"18","author":"HG Han","year":"2010","unstructured":"Han HG, Qiao JF (2010) A self-organizing fuzzy neural network based on a growing-and-pruning algorithm. IEEE Trans Fuzzy Syst 18(6):1129\u20131143","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"5","key":"5276_CR53","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/37.621467","volume":"17","author":"G Chen","year":"1997","unstructured":"Chen G, Chen Y, Ogrnen H (1997) Identifying chaotic systems via a Wiener-type cascade model. IEEE Control Syst 17(5):29\u201336","journal-title":"IEEE Control Syst"},{"issue":"1","key":"5276_CR54","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/BF01608556","volume":"50","author":"M H\u00e9non","year":"1976","unstructured":"H\u00e9non M (1976) A two-dimensional mapping with a strange attractor. Commun Math Phys 50(1):69\u201377","journal-title":"Commun Math Phys"},{"issue":"10","key":"5276_CR55","doi-asserted-by":"crossref","first-page":"6163","DOI":"10.1007\/s00521-018-3420-6","volume":"31","author":"J Qiao","year":"2019","unstructured":"Qiao J, Wang L, Yang C (2019) Adaptive lasso echo state network based on modified Bayesian information criterion for nonlinear system modeling. Neural Comput Appl 31(10):6163\u20136177","journal-title":"Neural Comput Appl"},{"issue":"4","key":"5276_CR56","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/91.940970","volume":"9","author":"S Wu","year":"2001","unstructured":"Wu S, Er MJ, Gao Y (2001) A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578\u2013594","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"5276_CR57","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1109\/TCYB.2013.2260537","volume":"44","author":"HG Han","year":"2014","unstructured":"Han HG, Wu XL, Qiao JF (2014) Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm. IEEE Trans Cybern 44(4):554\u2013564","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"5276_CR58","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TNNLS.2012.2231436","volume":"24","author":"YY Lin","year":"2013","unstructured":"Lin YY, Chang JY, Lin CT (2013) Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network. IEEE Trans Neural Netw Learn Syst 24(2):310\u2013321","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"19","key":"5276_CR59","doi-asserted-by":"crossref","first-page":"2552","DOI":"10.1016\/j.fss.2010.04.006","volume":"161","author":"CF Juang","year":"2010","unstructured":"Juang CF, Lin YY, Tu CC (2010) A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing. Fuzzy Sets Syst 161(19):2552\u20132568","journal-title":"Fuzzy Sets Syst"},{"issue":"2","key":"5276_CR60","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/91.995118","volume":"10","author":"CF Juang","year":"2002","unstructured":"Juang CF (2002) A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms. IEEE Trans Fuzzy Syst 10(2):155\u2013170","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"16\u201318","key":"5276_CR61","doi-asserted-by":"crossref","first-page":"3818","DOI":"10.1016\/j.neucom.2009.05.006","volume":"72","author":"N Wang","year":"2009","unstructured":"Wang N, Er MJ, Meng XY (2009) A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks. Neurocomputing 72(16\u201318):3818\u20133829","journal-title":"Neurocomputing"},{"issue":"2","key":"5276_CR62","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/3477.990874","volume":"32","author":"PA Mastorocostas","year":"2002","unstructured":"Mastorocostas PA, Theocharis JB (2002) A recurrent fuzzy-neural model for dynamic system identification. IEEE Trans Syst Man Cybern Part B Cybern 32(2):176\u2013190","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05276-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05276-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05276-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T23:35:45Z","timestamp":1630366545000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05276-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,31]]},"references-count":62,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["5276"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05276-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,31]]},"assertion":[{"value":"12 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}