{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T03:43:59Z","timestamp":1782186239872,"version":"3.54.5"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2020,11,22]],"date-time":"2020-11-22T00:00:00Z","timestamp":1606003200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,22]],"date-time":"2020-11-22T00:00:00Z","timestamp":1606003200000},"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 Comput &amp; Applic"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s00521-020-05526-x","type":"journal-article","created":{"date-parts":[[2020,11,22]],"date-time":"2020-11-22T09:10:17Z","timestamp":1606036217000},"page":"7875-7892","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["A novel dynamic recurrent functional link neural network-based identification of nonlinear systems using Lyapunov stability analysis"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7172-1081","authenticated-orcid":false,"given":"Rajesh","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Smriti","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,22]]},"reference":[{"key":"5526_CR1","doi-asserted-by":"crossref","unstructured":"Gish H (1990) A probabilistic approach to the understanding and training of neural network classifiers. In: 1990 International conference on acoustics, speech, and signal processing, 1990. ICASSP-90. IEEE, pp. 1361\u20131364","DOI":"10.1109\/ICASSP.1990.115636"},{"issue":"4","key":"5526_CR2","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/5326.897072","volume":"30","author":"GP Zhang","year":"2000","unstructured":"Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 30(4):451\u2013462","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"issue":"2","key":"5526_CR3","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/72.485683","volume":"7","author":"YR Park","year":"1996","unstructured":"Park YR, Murray TJ, Chen C (1996) Predicting sun spots using a layered perceptron neural network. IEEE Trans Neural Networks 7(2):501\u2013505","journal-title":"IEEE Trans Neural Networks"},{"issue":"3","key":"5526_CR4","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1016\/S0377-2217(00)00171-5","volume":"132","author":"M Qi","year":"2001","unstructured":"Qi M, Zhang GP (2001) An investigation of model selection criteria for neural network time series forecasting. Eur J Oper Res 132(3):666\u2013680","journal-title":"Eur J Oper Res"},{"issue":"1","key":"5526_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/72.80202","volume":"1","author":"KS Narendra","year":"1990","unstructured":"Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Networks 1(1):4\u201327","journal-title":"IEEE Trans Neural Networks"},{"key":"5526_CR6","doi-asserted-by":"publisher","unstructured":"Kumar R, Srivastava S, Gupta JRP (2017) Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using Lyapunov stability criterion, vol 67. ISA transactions, pp 407\u2013427. https:\/\/doi.org\/10.1016\/j.isatra.2017.01.022","DOI":"10.1016\/j.isatra.2017.01.022"},{"key":"5526_CR7","doi-asserted-by":"crossref","unstructured":"Patra J.\u00a0C, Bornand C (2010) Nonlinear dynamic system identification using Legendre neural network. In: The 2010 international joint conference on neural networks (IJCNN), IEEE, pp 1\u20137","DOI":"10.1109\/IJCNN.2010.5596904"},{"issue":"2","key":"5526_CR8","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1080\/00207179208934317","volume":"56","author":"S Chen","year":"1992","unstructured":"Chen S, Billings S (1992) Neural networks for nonlinear dynamic system modelling and identification. Int J Control 56(2):319\u2013346","journal-title":"Int J Control"},{"issue":"23","key":"5526_CR9","doi-asserted-by":"publisher","first-page":"4972","DOI":"10.1364\/AO.26.004972","volume":"26","author":"CL Giles","year":"1987","unstructured":"Giles CL, Maxwell T (1987) Learning, invariance, and generalization in high-order neural networks. Appl Opt 26(23):4972\u20134978","journal-title":"Appl Opt"},{"issue":"3","key":"5526_CR10","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/0885-064X(91)90040-5","volume":"7","author":"SS Venkatesh","year":"1991","unstructured":"Venkatesh SS, Baldi P (1991) Programmed interactions in higher-order neural networks: maximal capacity. J Complex 7(3):316\u2013337","journal-title":"J Complex"},{"issue":"6","key":"5526_CR11","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/j.patrec.2004.09.029","volume":"26","author":"E Artyomov","year":"2005","unstructured":"Artyomov E, Yadid-Pecht O (2005) Modified high-order neural network for invariant pattern recognition. Pattern Recogn Lett 26(6):843\u2013851","journal-title":"Pattern Recogn Lett"},{"key":"5526_CR12","doi-asserted-by":"crossref","unstructured":"Hassim Y.\u00a0M.\u00a0M, Ghazali R (2013) Functional link neural network\u2013artificial bee colony for time series temperature prediction. In: International conference on computational science and its applications. Springer, pp 427\u2013437","DOI":"10.1007\/978-3-642-39637-3_34"},{"key":"5526_CR13","doi-asserted-by":"crossref","unstructured":"Patra JC, Chin WC, Meher PK, Chakraborty G (2008) Legendre-flann-based nonlinear channel equalization in wireless communication system. In: IEEE international conference on systems, man and cybernetics, 2008. SMC 2008. IEEE, pp 1826\u20131831","DOI":"10.1109\/ICSMC.2008.4811554"},{"key":"5526_CR14","doi-asserted-by":"publisher","unstructured":"Gupta T, Sachdeva SN (2019) Prediction of compressive and flexural strengths of jarosite mixed cement concrete pavements using artificial neural networks. Road Mater Pavement Des 1\u201322. https:\/\/doi.org\/10.1080\/14680629.2019.1702583","DOI":"10.1080\/14680629.2019.1702583"},{"issue":"11","key":"5526_CR15","doi-asserted-by":"publisher","first-page":"3327","DOI":"10.1007\/s00521-017-2907-x","volume":"30","author":"DM Sahoo","year":"2018","unstructured":"Sahoo DM, Chakraverty S (2018) Functional link neural network approach to solve structural system identification problems. Neural Comput Appl 30(11):3327\u20133338","journal-title":"Neural Comput Appl"},{"issue":"4","key":"5526_CR16","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1109\/TNN.2005.851786","volume":"16","author":"L Ma","year":"2005","unstructured":"Ma L, Khorasani K (2005) Constructive feedforward neural networks using hermite polynomial activation functions. IEEE Trans Neural Networks 16(4):821\u2013833","journal-title":"IEEE Trans Neural Networks"},{"issue":"8","key":"5526_CR17","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.1109\/TNNLS.2014.2360879","volume":"27","author":"BY Vyas","year":"2016","unstructured":"Vyas BY, Das B, Maheshwari RP (2016) Improved fault classification in series compensated transmission line: comparative evaluation of Chebyshev neural network training algorithms. IEEE Trans Neural Netw Learn Syst 27(8):1631\u20131642","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"13\u201315","key":"5526_CR18","doi-asserted-by":"publisher","first-page":"3046","DOI":"10.1016\/j.neucom.2009.04.001","volume":"72","author":"H Zhao","year":"2009","unstructured":"Zhao H, Zhang J (2009) Nonlinear dynamic system identification using pipelined functional link artificial recurrent neural network. Neurocomputing 72(13\u201315):3046\u20133054","journal-title":"Neurocomputing"},{"issue":"2","key":"5526_CR19","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/78.348134","volume":"43","author":"S Haykin","year":"1995","unstructured":"Haykin S, Li L (1995) Nonlinear adaptive prediction of nonstationary signals. IEEE Trans Signal Process 43(2):526\u2013535","journal-title":"IEEE Trans Signal Process"},{"issue":"15","key":"5526_CR20","doi-asserted-by":"publisher","first-page":"4447","DOI":"10.1007\/s00500-016-2447-9","volume":"21","author":"R Kumar","year":"2017","unstructured":"Kumar R, Srivastava S, Gupta J (2017) Modeling and adaptive control of nonlinear dynamical systems using radial basis function network. Soft Comput 21(15):4447\u20134463","journal-title":"Soft Comput"},{"key":"5526_CR21","doi-asserted-by":"crossref","unstructured":"Gao X, Gao X, Ovaska S (1996) A modified Elman neural network model with application to dynamical systems identification. In: 1996 IEEE international conference on systems, man and cybernetics. Information intelligence and systems (Cat. No. 96CH35929), vol\u00a02, IEEE, pp 1376\u20131381","DOI":"10.1109\/ICSMC.1996.571312"},{"issue":"3","key":"5526_CR22","first-page":"182","volume":"3","author":"E Diaconescu","year":"2008","unstructured":"Diaconescu E (2008) The use of narx neural networks to predict chaotic time series. Wseas Trans Comput Res 3(3):182\u2013191","journal-title":"Wseas Trans Comput Res"},{"key":"5526_CR23","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"1994","unstructured":"Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall PTR, Upper Saddle River"},{"issue":"1","key":"5526_CR24","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s00500-018-3235-5","volume":"23","author":"R Kumar","year":"2019","unstructured":"Kumar R, Srivastava S, Gupta J, Mohindru A (2019) Comparative study of neural networks for dynamic nonlinear systems identification. Soft Comput 23(1):101\u2013114","journal-title":"Soft Comput"},{"issue":"7","key":"5526_CR25","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1016\/j.jfranklin.2013.04.020","volume":"350","author":"RH Abiyev","year":"2013","unstructured":"Abiyev RH, Kaynak O, Kayacan E (2013) A type-2 fuzzy wavelet neural network for system identification and control. J Frankl Inst 350(7):1658\u20131685","journal-title":"J Frankl Inst"},{"issue":"8","key":"5526_CR26","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1162\/NECO_a_00858","volume":"28","author":"S Mall","year":"2016","unstructured":"Mall S, Chakraverty S (2016) Hermite functional link neural network for solving the van der pol-duffing oscillator equation. Neural Comput 28(8):1574\u20131598","journal-title":"Neural Comput"},{"issue":"2","key":"5526_CR27","first-page":"231","volume":"10","author":"A Raudys","year":"1999","unstructured":"Raudys A, Mockus J (1999) Comparison of arma and multilayer perceptron based methods for economic time series forecasting. Informatica 10(2):231\u2013244","journal-title":"Informatica"},{"issue":"1\u20133","key":"5526_CR28","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.neucom.2009.07.005","volume":"73","author":"C-M Lee","year":"2009","unstructured":"Lee C-M, Ko C-N (2009) Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm. Neurocomputing 73(1\u20133):449\u2013460","journal-title":"Neurocomputing"},{"key":"5526_CR29","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1016\/j.asoc.2014.06.027","volume":"23","author":"J Wang","year":"2014","unstructured":"Wang J, Zhang W, Li Y, Wang J, Dang Z (2014) Forecasting wind speed using empirical mode decomposition and Elman neural network. Appl Soft Comput 23:452\u2013459","journal-title":"Appl Soft Comput"},{"issue":"16\u201318","key":"5526_CR30","doi-asserted-by":"publisher","first-page":"3335","DOI":"10.1016\/j.neucom.2008.01.030","volume":"71","author":"JMP Menezes Jr","year":"2008","unstructured":"Menezes JMP Jr, Barreto GA (2008) Long-term time series prediction with the Narx network: an empirical evaluation. Neurocomputing 71(16\u201318):3335\u20133343","journal-title":"Neurocomputing"},{"issue":"1","key":"5526_CR31","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/TFUZZ.1993.390281","volume":"1","author":"M Sugeno","year":"1993","unstructured":"Sugeno M, Yasukawa T (1993) A fuzzy-logic-based approach to qualitative modeling. IEEE Trans Fuzzy Syst 1(1):7","journal-title":"IEEE Trans Fuzzy Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05526-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05526-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05526-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T06:12:47Z","timestamp":1624515167000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05526-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,22]]},"references-count":31,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["5526"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05526-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,22]]},"assertion":[{"value":"25 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 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":"There is no conflict of interest among the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"no agency has funded this research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}