{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T08:25:40Z","timestamp":1742804740342},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,7,12]],"date-time":"2020-07-12T00:00:00Z","timestamp":1594512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,12]],"date-time":"2020-07-12T00:00:00Z","timestamp":1594512000000},"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":"publisher","award":["11702179"],"award-info":[{"award-number":["11702179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51605315"],"award-info":[{"award-number":["51605315"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Independent Project of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures","award":["No. ZZ2020-42"],"award-info":[{"award-number":["No. ZZ2020-42"]}]},{"name":"Major Program of the National Natural Science Foundation of China","award":["11790282"],"award-info":[{"award-number":["11790282"]}]},{"name":"Preferred Hebei Postdoctoral Research Project","award":["B2019003017"],"award-info":[{"award-number":["B2019003017"]}]},{"name":"Young Top-Notch Talents Program of Higher School in Hebei Province","award":["BJ2019035"],"award-info":[{"award-number":["BJ2019035"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s10489-020-01773-6","type":"journal-article","created":{"date-parts":[[2020,7,12]],"date-time":"2020-07-12T06:02:57Z","timestamp":1594533777000},"page":"4176-4194","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A derived least square fast learning network model"],"prefix":"10.1007","volume":"50","author":[{"given":"Meiqi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sixian","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enli","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaopu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,12]]},"reference":[{"issue":"8","key":"1773_CR1","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TNNLS.2015.2418739","volume":"27","author":"R Ak","year":"2015","unstructured":"Ak R, Fink O, Zio E (2015) Two machine learning approaches for short-term wind speed time-series prediction. IEEE T Neur Net Lear 27(8):1734\u20131747","journal-title":"IEEE T Neur Net Lear"},{"key":"1773_CR2","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.neunet.2016.09.004","volume":"85","author":"K Anam","year":"2017","unstructured":"Anam K, Al-Jumaily A (2017) Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees. Neural Netw 85:51\u201368","journal-title":"Neural Netw"},{"issue":"2","key":"1773_CR3","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1109\/18.661502","volume":"44","author":"PL Bartlett","year":"1998","unstructured":"Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE T Inform Theory 44(2):525\u2013536","journal-title":"IEEE T Inform Theory"},{"issue":"1","key":"1773_CR4","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s00521-014-1559-3","volume":"27","author":"J Chen","year":"2016","unstructured":"Chen J, Chen H, Wan X, Zheng G (2016) Mr -elm : a mapreduce-based framework for large-scale elm training in big data era. Neural Comput Appl 27(1):101\u2013110","journal-title":"Neural Comput Appl"},{"key":"1773_CR5","doi-asserted-by":"crossref","unstructured":"Chen Z, Gryllias K, Li W (2019) Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mech Syst Signal Process 133","DOI":"10.1016\/j.ymssp.2019.106272"},{"key":"1773_CR6","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neunet.2019.03.004","volume":"115","author":"H Dai","year":"2019","unstructured":"Dai H, Cao J, Wang T, Deng M, Yang Z (2019) Multilayer one-class extreme learning machine. Neural Netw 115:11\u201322","journal-title":"Neural Netw"},{"issue":"2","key":"1773_CR7","first-page":"20301","volume":"58","author":"C Deng","year":"2015","unstructured":"Deng C, Huang GB, Xu J, Tang J (2015) Extreme learning machines: new trends and applications. Sci China (Inform Sci) 58(2):20301\u2013020301","journal-title":"Sci China (Inform Sci)"},{"key":"1773_CR8","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","volume":"61","author":"G Huang","year":"2015","unstructured":"Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32\u201348","journal-title":"Neural Netw"},{"key":"1773_CR9","first-page":"985","volume":"2","author":"GB Huang","year":"2004","unstructured":"Huang GB, Zhu QY, Siew C (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural netw 2:985\u2013990","journal-title":"Neural netw"},{"issue":"1-3","key":"1773_CR10","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1-3):489\u2013501","journal-title":"Neurocomputing"},{"issue":"2","key":"1773_CR11","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2011","unstructured":"Huang GB, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE T Syst Man Cy B) 42(2):513\u2013529","journal-title":"IEEE T Syst Man Cy B)"},{"key":"1773_CR12","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.neunet.2016.12.002","volume":"87","author":"J Kim","year":"2017","unstructured":"Kim J, Kim J, Jang G, Lee M (2017) Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Netw 87:109\u2013121","journal-title":"Neural Netw"},{"key":"1773_CR13","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.neucom.2017.03.092","volume":"277","author":"N Kumar","year":"2018","unstructured":"Kumar N, Savitha R, Mamun A (2018) Ocean wave height prediction using ensemble of extreme learning machine. Neurocomputing 277:12\u201320","journal-title":"Neurocomputing"},{"issue":"4","key":"1773_CR14","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/s00521-017-3142-1","volume":"31","author":"Y Kutlu","year":"2019","unstructured":"Kutlu Y, Yay\u0131k A, Yildirim E, Yildirim S (2019) Lu triangularization extreme learning machine in eeg cognitive task classification. Neural Comput Appl 31(4):1117\u20131126","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1773_CR15","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s00500-014-1486-3","volume":"20","author":"G Li","year":"2016","unstructured":"Li G, Niu P (2016) Combustion optimization of a coal-fired boiler with double linear fast learning network. Soft Comput 20(1):149\u2013156","journal-title":"Soft Comput"},{"issue":"7-8","key":"1773_CR16","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1007\/s00521-013-1398-7","volume":"24","author":"G Li","year":"2014","unstructured":"Li G, Niu P, Duan X, Zhang X (2014a) Fast learning network: a novel artificial neural network with a fast learning speed. Neural Comput Applic 24(7-8):1683\u20131695","journal-title":"Neural Comput Applic"},{"key":"1773_CR17","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neunet.2013.12.006","volume":"51","author":"GQ Li","year":"2014","unstructured":"Li GQ, Niu PF, Wang HB, Liu YC (2014b) Least square fast learning network for modeling the combustion efficiency of a 300wm coal-fired boiler. Neural Netw 51:57\u201366","journal-title":"Neural Netw"},{"key":"1773_CR18","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.neucom.2019.03.084","volume":"350","author":"K Li","year":"2019","unstructured":"Li K, Xiong M, Li F, et al. (2019) A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine. Neurocomputing 350:261\u2013270","journal-title":"Neurocomputing"},{"issue":"8","key":"1773_CR19","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1007\/s00521-016-2195-x","volume":"28","author":"Z Li","year":"2017","unstructured":"Li Z, Fan X, Chen G, Yang G, Sun Y (2017) Optimization of iron ore sintering process based on elm model and multi-criteria evaluation. Neural Comput Applic 28(8):2247\u20132253","journal-title":"Neural Comput Applic"},{"key":"1773_CR20","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.knosys.2017.09.013","volume":"136","author":"YP Ma","year":"2017","unstructured":"Ma YP, Niu PF, Zhang XX, Li GQ (2017) Research and application of quantum-inspired double parallel feed-forward neural network. Knowl-Based Syst 136:140\u2013149","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"1773_CR21","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1007\/s00521-016-2754-1","volume":"30","author":"A Maliha","year":"2018","unstructured":"Maliha A, Yusof R, Shapiai M (2018) Extreme learning machine for structured output spaces. Neural Comput Applic 30(4):1251\u20131264","journal-title":"Neural Comput Applic"},{"key":"1773_CR22","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.neunet.2016.04.008","volume":"80","author":"B Mirza","year":"2016","unstructured":"Mirza B, Lin Z (2016) Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification. Neural Netw 80:79\u201394","journal-title":"Neural Netw"},{"key":"1773_CR23","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.neucom.2017.12.030","volume":"282","author":"D Nayak","year":"2018","unstructured":"Nayak D, Dash R, Majhi B (2018) Discrete ripplet-ii transform and modified pso based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing 282:232\u2013247","journal-title":"Neurocomputing"},{"key":"1773_CR24","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.neunet.2018.05.011","volume":"105","author":"B Raghuwanshi","year":"2018","unstructured":"Raghuwanshi B, Shukla S (2018) Class-specific extreme learning machine for handling binary class imbalance problem. Neural Netw 105:206\u2013217","journal-title":"Neural Netw"},{"issue":"3","key":"1773_CR25","first-page":"1","volume":"5","author":"D Rumelhart","year":"1988","unstructured":"Rumelhart D, Hinton G, Williams R (1988) Learning representations by back-propagating errors. Cognitive modeling 5 (3):1","journal-title":"Cognitive modeling"},{"issue":"1","key":"1773_CR26","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF02479751","volume":"24","author":"N Shinozaki","year":"1972","unstructured":"Shinozaki N, Sibuya M, Tanabe K (1972) Numerical algorithms for the moore-penrose inverse of a matrix: direct methods. Ann Inst Stat Math 24(1):193\u2013203","journal-title":"Ann Inst Stat Math"},{"issue":"1","key":"1773_CR27","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/S0165-1889(02)00157-4","volume":"28","author":"Y Singh","year":"2003","unstructured":"Singh Y, Chandra P (2003) A class+\u20091 sigmoidal activation functions for ffanns. J Econ Dyn Control 28(1):183\u2013187","journal-title":"J Econ Dyn Control"},{"issue":"1","key":"1773_CR28","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1137\/0711008","volume":"11","author":"T S\u00f6derstr\u00f6m","year":"1974","unstructured":"S\u00f6derstr\u00f6m T, Stewart G (1974) On the numerical properties of an iterative method for computing the moore\u2013penrose generalized inverse. SIAM J Numer Anal 11(1):61\u201374","journal-title":"SIAM J Numer Anal"},{"key":"1773_CR29","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2016.09.120","volume":"261","author":"W Xiao","year":"2017","unstructured":"Xiao W, Zhang J, Li Y, Zhang S, Yang W (2017) Class-specific cost regulation extreme learning machine for imbalanced classification. Neurocomputing 261:70\u201382","journal-title":"Neurocomputing"},{"key":"1773_CR30","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neucom.2019.03.079","volume":"355","author":"J Xie","year":"2019","unstructured":"Xie J, Liu S, Dai H (2019) Manifold regularization based distributed semi-supervised learning algorithm using extreme learning machine over time-varying network. Neurocomputing 355:24\u201334","journal-title":"Neurocomputing"},{"key":"1773_CR31","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2019.05.039","volume":"134","author":"H Yildirim","year":"2019","unstructured":"Yildirim H, \u00d6zkale M (2019) The performance of elm based ridge regression via the regularization parameters. Expert Syst Appl 134:225\u2013233","journal-title":"Expert Syst Appl"},{"key":"1773_CR32","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.neucom.2016.06.078","volume":"261","author":"Y Yu","year":"2017","unstructured":"Yu Y, Sun Z (2017) Sparse coding extreme learning machine for classification. Neurocomputing 261:50\u201356","journal-title":"Neurocomputing"},{"key":"1773_CR33","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.neucom.2018.05.057","volume":"311","author":"J Zhang","year":"2018","unstructured":"Zhang J, Xiao W, Li Y, Zhang S (2018) Residual compensation extreme learning machine for regression. Neurocomputing 311:126\u2013136","journal-title":"Neurocomputing"},{"issue":"3-4","key":"1773_CR34","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s00521-011-0808-y","volume":"22","author":"W Zheng","year":"2013","unstructured":"Zheng W, Qian Y, Lu H (2013) Text categorization based on regularization extreme learning machine. Neural Comput Applic 22(3-4):447\u2013456","journal-title":"Neural Comput Applic"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01773-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01773-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01773-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T23:43:57Z","timestamp":1626047037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01773-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,12]]},"references-count":34,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1773"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01773-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,12]]},"assertion":[{"value":"12 July 2020","order":1,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}