{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:14:51Z","timestamp":1773249291748,"version":"3.50.1"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T00:00:00Z","timestamp":1580774400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T00:00:00Z","timestamp":1580774400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"the General Program of National Natural Science of China","award":["61873107"],"award-info":[{"award-number":["61873107"]}]},{"name":"\u201c333\u201d Project in Jiangsu Province","award":["BRA2019285"],"award-info":[{"award-number":["BRA2019285"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1007\/s10489-020-01645-z","type":"journal-article","created":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T03:05:36Z","timestamp":1580785536000},"page":"1657-1672","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Nonlinear system modeling using self-organizing fuzzy neural networks for industrial applications"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2924-9937","authenticated-orcid":false,"given":"Hongbiao","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,4]]},"reference":[{"issue":"7","key":"1645_CR1","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TSMC.2016.2560147","volume":"47","author":"AA Adeniran","year":"2017","unstructured":"Adeniran AA, Ferik SE (2017) Modeling and identification of nonlinear systems: a review of the multimodel approach\u2013part 1. IEEE Trans Syst Man Cy-S 47(7):1149\u20131159","journal-title":"IEEE Trans Syst Man Cy-S"},{"key":"1645_CR2","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.asoc.2018.02.027","volume":"66","author":"H Moayedi","year":"2018","unstructured":"Moayedi H, Hayati S (2018) Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput 66:208\u2013219","journal-title":"Appl Soft Comput"},{"issue":"4","key":"1645_CR3","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/TNNLS.2017.2665581","volume":"29","author":"W He","year":"2018","unstructured":"He W, Dong Y (2018) Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Transactions on Neural Networks and Learning Systems 29(4):1174\u20131186","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"4","key":"1645_CR4","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/TCYB.2018.2799683","volume":"49","author":"DP Li","year":"2019","unstructured":"Li DP, Liu YJ, Tong S, Chen CP, Li DJ (2019) Neural networks-based adaptive control for nonlinear state constrained systems with input delay. IEEE Transactions on Cybernetics 49(4):1249\u20131258","journal-title":"IEEE Transactions on Cybernetics"},{"key":"1645_CR5","doi-asserted-by":"publisher","unstructured":"Tan KH, Lin FJ, Shih CM, Kuo CN (2019) Intelligent control of microgrid with virtual inertia using recurrent probabilistic wavelet fuzzy neural network. IEEE Trans Power Electron. https:\/\/doi.org\/10.1109\/TPEL.2019.2954740","DOI":"10.1109\/TPEL.2019.2954740"},{"issue":"10","key":"1645_CR6","doi-asserted-by":"crossref","first-page":"8104","DOI":"10.1109\/TIE.2018.2884195","volume":"66","author":"QB Lin","year":"2019","unstructured":"Lin QB, Chen SC, Lin CM (2019) Parametric fault diagnosis based on fuzzy cerebellar model neural networks. IEEE Trans Ind Electron 66(10):8104\u20138115","journal-title":"IEEE Trans Ind Electron"},{"key":"1645_CR7","doi-asserted-by":"crossref","unstructured":"Al Sayaydeh ON, Mohammed MF, Alhroob E, Tao H, Lim CP (2019) A refined fuzzy min-max neural network with new learning procedures for pattern classification. IEEE Trans Fuzzy Syst","DOI":"10.1109\/TFUZZ.2019.2939975"},{"issue":"6","key":"1645_CR8","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1109\/TFUZZ.2018.2870574","volume":"27","author":"GY Lai","year":"2019","unstructured":"Lai GY, Zhang Y, Liu Z, Chen CP (2019) Indirect adaptive fuzzy control design with guaranteed tracking error performance for uncertain canonical nonlinear systems. IEEE Trans Fuzzy Syst 27(6):1139\u20131150","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"1645_CR9","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TIE.2019.2902790","volume":"67","author":"SW Xie","year":"2020","unstructured":"Xie SW, Xie YF, Ying H, Jiang ZH, Gui WH (2020) Neuro-fuzzy-based plant-wide hierarchical coordinating optimization and control: an application to zinc hydrometallurgy plant. IEEE Trans Ind Electron 67(3):2207\u20132218","journal-title":"IEEE Trans Ind Electron"},{"key":"1645_CR10","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.conengprac.2013.09.005","volume":"22","author":"BOS Teixeira","year":"2014","unstructured":"Teixeira BOS, Castro WS, Teixeira AF, Aguirre LA (2014) Data-driven soft sensor of downhole pressure for a gas-lift oil well. Control Eng Pract 22:34\u201343","journal-title":"Control Eng Pract"},{"issue":"4","key":"1645_CR11","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.1109\/TIE.2016.2645498","volume":"64","author":"RD Zhang","year":"2017","unstructured":"Zhang RD, Tao JL (2017) Data-driven modeling using improved multi-objective optimization based neural network for coke furnace system. IEEE Trans Ind Electron 64(4):3147\u20133155","journal-title":"IEEE Trans Ind Electron"},{"issue":"6","key":"1645_CR12","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1109\/TFUZZ.2014.2300134","volume":"22","author":"W Pedrycz","year":"2014","unstructured":"Pedrycz W, Izakian H (2014) Cluster-centric fuzzy modeling. IEEE Trans Fuzzy Syst 22(6):1585\u20131597","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"1645_CR13","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/TFUZZ.2017.2686352","volume":"26","author":"YL Wei","year":"2018","unstructured":"Wei YL, Qiu JB, Karimi HR (2018) Fuzzy-affine-model-based memory filter design of nonlinear systems with time-varying delay. IEEE Trans Fuzzy Syst 26(2):504\u2013517","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"11","key":"1645_CR14","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1109\/TFUZZ.2019.2895572","volume":"27","author":"P Xu","year":"2019","unstructured":"Xu P, Deng ZH, Cui C, Zhang T, Choi KS, Suhang GSH, Wang J, Wang ST (2019) Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning. IEEE Trans Fuzzy Syst 27(11):2176\u20132188","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1645_CR15","doi-asserted-by":"crossref","unstructured":"Alghamdi M, Angelov P, Gimenez R, Rufino M, Soares E (2019) Self-organising and self-learning model for soybean yield prediction. In: 2019 sixth international conference on social networks analysis, management and security (SNAMS 2019). IEEE, pp 441\u2013446","DOI":"10.1109\/SNAMS.2019.8931888"},{"issue":"4","key":"1645_CR16","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1109\/TR.2015.2427156","volume":"64","author":"J Liu","year":"2015","unstructured":"Liu J, Vitelli V, Zio E, Seraoui R (2015) A novel dynamic-weighted probabilistic support vector regression-based ensemble for prognostics of time series data. IEEE Trans Reliab 64(4):1203\u20131213","journal-title":"IEEE Trans Reliab"},{"key":"1645_CR17","doi-asserted-by":"crossref","unstructured":"Cao LL, Xu LH, Goodman ED, Bao CT, Zhu SW (2019) Evolutionary dynamic multiobjective optimization assisted by a support vector regression predictor. IEEE Trans Evol Comput","DOI":"10.1109\/TEVC.2019.2925722"},{"issue":"12","key":"1645_CR18","first-page":"2718","volume":"27","author":"SD Liu","year":"2016","unstructured":"Liu SD, Hou ZS, Yin CK (2016) Data-driven modeling for. UGI gasification processes via an enhanced genetic BP neural network with link switches IEEE Trans Neur Net Lear 27(12):2718\u20132729","journal-title":"UGI gasification processes via an enhanced genetic BP neural network with link switches IEEE Trans Neur Net Lear"},{"issue":"1","key":"1645_CR19","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s00500-018-3235-5","volume":"23","author":"R Kumar","year":"2019","unstructured":"Kumar R, Srivastava S, Gupta JRP, Mohindru A (2019) Comparative study of neural networks for dynamic nonlinear systems identification. Soft Comput 23(1):101\u2013114","journal-title":"Soft Comput"},{"key":"1645_CR20","doi-asserted-by":"crossref","unstructured":"Nunez F, Langarica S, Diaz P, Torres M, Salas JC (2019) Neural Network-Based Model Predictive Control of a Paste Thickener over an Industrial Internet Platform. IEEE Transactions on Industrial Informatics","DOI":"10.1109\/TII.2019.2953275"},{"issue":"2","key":"1645_CR21","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 Transactions on Neural Networks and Learning Systems 24(2):310\u2013321","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1645_CR22","doi-asserted-by":"crossref","unstructured":"Zhang ZJ, Yan ZY (2019) An adaptive fuzzy recurrent neural network for solving non-repetitive motion problem of redundant robot manipulators. IEEE Trans Fuzzy Syst","DOI":"10.1109\/TFUZZ.2019.2914618"},{"issue":"2","key":"1645_CR23","first-page":"358","volume":"30","author":"SQ Wu","year":"2000","unstructured":"Wu SQ, Er MJ (2000) Dynamic fuzzy neural networks-a novel approach to function approximation. IEEE Trans Cybernetics 30(2):358\u2013364","journal-title":"IEEE Trans Cybernetics"},{"issue":"4","key":"1645_CR24","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/91.940970","volume":"9","author":"SQ Wu","year":"2001","unstructured":"Wu SQ, 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":"16","key":"1645_CR25","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):3818\u20133829","journal-title":"Neurocomputing"},{"issue":"1","key":"1645_CR26","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s11063-011-9181-1","volume":"34","author":"N Wang","year":"2011","unstructured":"Wang N (2011) A generalized ellipsoidal basis function based online self-constructing fuzzy neural network. Neural Process Lett 34(1):13\u201337","journal-title":"Neural Process Lett"},{"issue":"6","key":"1645_CR27","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1109\/TFUZZ.2009.2029569","volume":"17","author":"JJ Rubio","year":"2009","unstructured":"Rubio JJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296\u20131309","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"6","key":"1645_CR28","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":"4","key":"1645_CR29","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 Cybernetics 44(4):554\u2013564","journal-title":"IEEE Trans Cybernetics"},{"key":"1645_CR30","doi-asserted-by":"publisher","unstructured":"de Campos Souza PV, Nunes CFG, Guimares AJ, Rezende TS, Araujo VS, Arajuo VJS (2019) Self-organized direction aware for regularized fuzzy neural networks. Evolving Systems. Available: https:\/\/doi.org\/10.1007\/s12530-019-09278-5","DOI":"10.1007\/s12530-019-09278-5"},{"key":"1645_CR31","doi-asserted-by":"crossref","first-page":"3221","DOI":"10.1007\/s10489-019-01449-w","volume":"49","author":"PV de Campos Souza","year":"2019","unstructured":"de Campos Souza PV, Guimaraes AJ, Araujo VS, Rezende TS, Araujo VJS (2019) Incremental regularized data density-based clustering neural networks to aid in the construction of effort forecasting systems in software development. Appl Intell 49:3221\u20133234","journal-title":"Appl Intell"},{"key":"1645_CR32","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.ins.2019.07.006","volume":"503","author":"XW Gu","year":"2019","unstructured":"Gu XW, Angelov P, Rong HJ (2019) Local optimality of self-organising neuro-fuzzy inference systems. Inf Sci 503:351\u2013380","journal-title":"Inf Sci"},{"issue":"3","key":"1645_CR33","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/TFUZZ.2018.2863650","volume":"27","author":"J Zhao","year":"2019","unstructured":"Zhao J, Lin CM (2019) Wavelet-TSK-type fuzzy cerebellar model neural network for uncertain nonlinear systems. IEEE Trans Fuzzy Syst 27(3):549\u2013558","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"62","key":"1645_CR34","doi-asserted-by":"crossref","first-page":"114","DOI":"10.4114\/intartif.vol22iss63pp114-133","volume":"21","author":"PV de Campos Souza","year":"2018","unstructured":"de Campos Souza PV, Guimaraes AJ, Ara\u00fajo VS, Rezende TS, Ara\u00fajo VJS (2018) Fuzzy neural networks based on fuzzy logic neurons regularized by resampling techniques and regularization theory for regression problems. Intel Artif 21(62):114\u2013133","journal-title":"Intel Artif"},{"key":"1645_CR35","unstructured":"Han HG, Wu XL, Liu Z, Qiao JF (2019) Data-knowledge-based fuzzy neural network for nonlinear system identification. IEEE Trans Fuzzy Syst"},{"key":"1645_CR36","doi-asserted-by":"crossref","first-page":"211","DOI":"10.3233\/HIS-140196","volume":"11","author":"J Soto","year":"2014","unstructured":"Soto J, Melin P, Castillo O (2014) Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators. Int J Hybrid Intell Syst 11:211\u2013226","journal-title":"Int J Hybrid Intell Syst"},{"issue":"2","key":"1645_CR37","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/TFUZZ.2018.2858740","volume":"27","author":"A Rubio-Solis","year":"2018","unstructured":"Rubio-Solis A, Melin P, Martinez-Hernandez U, Panoutsos G (2018) General type-2 radial basis function neural network: a data-driven fuzzy model. IEEE Trans Fuzzy Syst 27(2):333\u2013347","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"1645_CR38","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1007\/s40815-017-0443-6","volume":"20","author":"J Soto","year":"2018","unstructured":"Soto J, Melin P, Castillo O (2018) A new approach for time series prediction using ensembles of IT2FNN models with optimization of fuzzy integrators. International Journal of Fuzzy Systems 20(3):701\u2013728","journal-title":"International Journal of Fuzzy Systems"},{"issue":"5","key":"1645_CR39","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1007\/s40815-019-00642-w","volume":"21","author":"J Soto","year":"2019","unstructured":"Soto J, Castillo O, Melin P, Pedrycz W (2019) A new approach to multiple time series prediction using MIMO fuzzy aggregation models with modular neural networks. International Journal of Fuzzy Systems 21(5):1629\u20131648","journal-title":"International Journal of Fuzzy Systems"},{"key":"1645_CR40","doi-asserted-by":"publisher","unstructured":"Khater AA, El-Nagar AM, El-Bardini M, El-Rabaie N (2019) A novel structure of actor-critic learning based on an interval type-2 TSK fuzzy neural network. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2019.2949554","DOI":"10.1109\/TFUZZ.2019.2949554"},{"issue":"9","key":"1645_CR41","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TITS.2016.2643005","volume":"18","author":"JJ Tang","year":"2017","unstructured":"Tang JJ, Liu F, Zou YJ, Zhang WB, Wang YH (2017) An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Trans Intell Transp Syst 18(9):2340\u20132349","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"8","key":"1645_CR42","doi-asserted-by":"crossref","first-page":"3452","DOI":"10.1109\/TNNLS.2017.2729589","volume":"29","author":"W Huang","year":"2017","unstructured":"Huang W, Oh SK, Pedrycz W (2017) Hybrid fuzzy wavelet neural networks architecture based on polynomial neural networks and fuzzy set\/relation inference-based wavelet neurons. IEEE Transactions on Neural Networks and Learning Systems 29(8):3452\u20133462","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"5","key":"1645_CR43","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JAS.2018.7511168","volume":"5","author":"JF Qiao","year":"2018","unstructured":"Qiao JF, Zhou HB (2018) Modeling of energy consumption and effluent quality using density peaks-based adaptive fuzzy neural network. IEEE\/CAA Journal of Automatica Sinica 5(5):968\u2013976","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"issue":"1","key":"1645_CR44","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TSMCC.2008.2002333","volume":"39","author":"CJ Lin","year":"2009","unstructured":"Lin CJ, Chen CH, Lin CT (2009) A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications. IEEE Trans Syst Man Cybern Part C Appl Rev 39(1):55\u201368","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"issue":"6","key":"1645_CR45","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1109\/TFUZZ.2006.877361","volume":"14","author":"G Leng","year":"2006","unstructured":"Leng G, McGinnity TM, Prasad G (2006) Design for self-organizing fuzzy neural networks based on genetic algorithms. IEEE Trans Fuzzy Syst 14(6):755\u2013766","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"7","key":"1645_CR46","doi-asserted-by":"crossref","first-page":"5882","DOI":"10.1109\/TIE.2017.2777415","volume":"65","author":"RD Zhang","year":"2018","unstructured":"Zhang RD, Tao JL (2018) A nonlinear fuzzy neural network modeling approach using an improved genetic algorithm. IEEE Trans Ind Electron 65(7):5882\u20135892","journal-title":"IEEE Trans Ind Electron"},{"issue":"6","key":"1645_CR47","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/TCST.2008.2008194","volume":"17","author":"W Yu","year":"2009","unstructured":"Yu W (2009) A novel fuzzy-neural-network modeling approach to crude-oil blending. IEEE Trans Control Syst Technol 17(6):1424\u20131431","journal-title":"IEEE Trans Control Syst Technol"},{"issue":"11","key":"1645_CR48","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TNN.2010.2073482","volume":"21","author":"BM Wilamowski","year":"2010","unstructured":"Wilamowski BM, Yu H (2010) Neural network learning without backpropagation. IEEE Trans Neur Net Lear 21(11):1793\u20131803","journal-title":"IEEE Trans Neur Net Lear"},{"issue":"3","key":"1645_CR49","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TFUZZ.2011.2175932","volume":"20","author":"M Davanipoor","year":"2012","unstructured":"Davanipoor M, Zekri M, Sheikholeslam F (2012) Fuzzy wavelet neural network with an accelerated hybrid learning algorithm. IEEE Trans Fuzzy Syst 20(3):463\u2013470","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1","key":"1645_CR50","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TFUZZ.2012.2200900","volume":"21","author":"WQ Zhao","year":"2013","unstructured":"Zhao WQ, Li K, Irwin GW (2013) A new gradient descent approach for local learning of fuzzy neural models. IEEE Trans Fuzzy Syst 21(1):30\u201344","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"8","key":"1645_CR51","doi-asserted-by":"crossref","first-page":"3436","DOI":"10.1109\/TII.2017.2777460","volume":"14","author":"C Lv","year":"2018","unstructured":"Lv C, Xiang Y, Zhang JZ, Na XX, Li YT, Liu T, Cao DP, Wang FY (2018) Levenberg\u2013Marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system. IEEE Transactions on Industrial Informatics 14(8):3436\u20133446","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"19","key":"1645_CR52","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.neucom.2016.07.003","volume":"214","author":"HG Han","year":"2016","unstructured":"Han HG, Ge LM, Qiao JF (2016) An adaptive second order fuzzy neural network for nonlinear system modeling. Neurocomputing 214(19):837\u2013847","journal-title":"Neurocomputing"},{"key":"1645_CR53","doi-asserted-by":"crossref","first-page":"132","DOI":"10.5004\/dwt.2019.23360","volume":"140","author":"HB Zhou","year":"2019","unstructured":"Zhou HB, Qiao JF (2019) Soft sensing of effluent ammonia nitrogen using rule automatic formation-based adaptive fuzzy neural network. Desalin Water Treat 140:132\u2013142","journal-title":"Desalin Water Treat"},{"issue":"3","key":"1645_CR54","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"},{"issue":"5","key":"1645_CR55","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1109\/TIE.2008.2010105","volume":"56","author":"FJ Lin","year":"2009","unstructured":"Lin FJ, Teng LT, Lin JW, Chen SY (2009) Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization. IEEE Trans Ind Electron 56(5):1557\u20131577","journal-title":"IEEE Trans Ind Electron"},{"key":"1645_CR56","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ins.2013.10.035","volume":"262","author":"RJ Kuo","year":"2014","unstructured":"Kuo RJ, Hung SY, Cheng WC (2014) Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system. Inf Sci 262:78\u201398","journal-title":"Inf Sci"},{"issue":"2","key":"1645_CR57","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/s10489-011-0327-7","volume":"37","author":"H Malek","year":"2012","unstructured":"Malek H, Ebadzadeh MM, Rahmati M (2012) Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm. Appl Intell 37(2):280\u2013289","journal-title":"Appl Intell"},{"issue":"7120","key":"1645_CR58","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1038\/nature05300","volume":"444","author":"NC Spitzer","year":"2006","unstructured":"Spitzer NC (2006) Electrical activity in early neuronal development. Nature 444(7120):707\u2013712","journal-title":"Nature"},{"issue":"7343","key":"1645_CR59","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1038\/nature09865","volume":"472","author":"NVDM Garc\u00eda","year":"2011","unstructured":"Garc\u00eda NVDM, Karayannis T, Fishell G (2011) Neuronal activity is required for the development of specific cortical interneuron subtypes. Nature 472(7343):351\u2013356","journal-title":"Nature"},{"key":"1645_CR60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2018.01.001","volume":"302","author":"JF Qiao","year":"2018","unstructured":"Qiao JF, Meng X, Li WJ (2018) An incremental neuronal-activity-based RBF neural network for nonlinear system modeling. Neurocomputing 302:1\u201311","journal-title":"Neurocomputing"},{"issue":"7","key":"1645_CR61","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1109\/TSP.2007.916137","volume":"56","author":"J Liu","year":"2008","unstructured":"Liu J, Liu XQ, Ma X (2008) First-order perturbation analysis of singular vectors in singular value decomposition. IEEE Trans Signal Proces 56(7):3044\u20133049","journal-title":"IEEE Trans Signal Proces"},{"issue":"4","key":"1645_CR62","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1137\/050639193","volume":"29","author":"Z Drma\u010d","year":"2008","unstructured":"Drma\u010d Z, Veseli\u0107 K (2008) New fast and accurate Jacobi SVD algorithm. I SIAM J Matrix Anal A 29(4):1322\u20131342","journal-title":"I SIAM J Matrix Anal A"},{"issue":"6","key":"1645_CR63","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1109\/TNN.2010.2045657","volume":"21","author":"BM Wilamowski","year":"2010","unstructured":"Wilamowski BM, Yu H (2010) Improved computation for Levenberg\u2013Marquardt training. IEEE Trans Neur Net Lear 21(6):930\u2013937","journal-title":"IEEE Trans Neur Net Lear"},{"issue":"2","key":"1645_CR64","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TFUZZ.2011.2173693","volume":"20","author":"CS Li","year":"2012","unstructured":"Li CS, Zhou JZ, Fu B, Kou PG, Xiao J (2012) T\u2013S fuzzy model identification with a gravitational search-based hyperplane clustering algorithm. IEEE Trans Fuzzy Syst 20(2):305\u2013317","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"8","key":"1645_CR65","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1109\/TIE.2008.924018","volume":"55","author":"RH Abiyev","year":"2008","unstructured":"Abiyev RH, Kaynak O (2008) Fuzzy wavelet neural networks for identification and control of dynamic plants\u2014a novel structure and a comparative study. IEEE Trans Ind Electron 55(8):3133\u20133140","journal-title":"IEEE Trans Ind Electron"},{"issue":"1","key":"1645_CR66","doi-asserted-by":"crossref","first-page":"219","DOI":"10.3390\/ijerph10010219","volume":"10","author":"E Lee","year":"2013","unstructured":"Lee E, Han S, Kim H (2013) Development of software sensors for determining total phosphorus and total nitrogen in waters. Int J Environ Res Public Health 10(1):219\u2013236","journal-title":"Int J Environ Res Public Health"},{"key":"1645_CR67","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.desal.2008.06.011","volume":"245","author":"Z Wang","year":"2009","unstructured":"Wang Z, Chu JS, Song Y, Cui YJ, Zhang H, Zhao XQ, Li ZH, Yao JM (2009) Influence of operating conditions on the efficiency of domestic wastewater treatment in membrane bioreactors. Desalination 245:73\u201381","journal-title":"Desalination"},{"issue":"1\u20133","key":"1645_CR68","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jenvman.2014.12.014","volume":"151","author":"FJ Chang","year":"2015","unstructured":"Chang FJ, Tsai YH, Chen PA, Coynel AG (2015) Modeling water quality in an urban river using hydrological factors\u2013data driven approaches. J Environ Manag 151(1\u20133):87\u201396","journal-title":"J Environ Manag"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01645-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-020-01645-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01645-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T01:00:30Z","timestamp":1722387630000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-020-01645-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,4]]},"references-count":68,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,5]]}},"alternative-id":["1645"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01645-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,4]]},"assertion":[{"value":"4 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}