{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T18:03:47Z","timestamp":1777658627454,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2017,5,20]],"date-time":"2017-05-20T00:00:00Z","timestamp":1495238400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61333002"],"award-info":[{"award-number":["61333002"]}],"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":["61673056"],"award-info":[{"award-number":["61673056"]}],"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":["61671054"],"award-info":[{"award-number":["61671054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1007\/s13042-017-0674-8","type":"journal-article","created":{"date-parts":[[2017,5,20]],"date-time":"2017-05-20T04:38:59Z","timestamp":1495255139000},"page":"1697-1706","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Prediction of the hot metal silicon content in blast furnace based on extreme learning machine"],"prefix":"10.1007","volume":"9","author":[{"given":"Haigang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixin","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianzhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,5,20]]},"reference":[{"key":"674_CR1","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/TFUZZ.2013.2269145","volume":"22","author":"CH Gao","year":"2014","unstructured":"Gao CH, Ge QH and Jian L (2014) Rule extraction from fuzzy-based blast furnace SVM multiclassifier for decision-making. IEEE Trans Fuzzy Syst 22:586\u2013596","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"674_CR2","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.2355\/isijinternational.52.2048","volume":"52","author":"XZ Chen","year":"2012","unstructured":"Chen XZ, Wei JD, Xu D (2012) 3-dimension imaging system of burden surface with 6-radars array in a blast furnace. ISIJ Int 52:2048\u20132054","journal-title":"ISIJ Int"},{"key":"674_CR3","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.2355\/isijinternational.45.1943","volume":"45","author":"T Bhattacharaya","year":"2005","unstructured":"Bhattacharaya T (2005) Prediction of silicon content in blast furnace hot metal using partial least squares (PLS). ISIJ Int 45:1943\u20131945","journal-title":"ISIJ Int"},{"key":"674_CR4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s12613-016-1206-3","volume":"23","author":"KX Jiao","year":"2016","unstructured":"Jiao KX, Zhang JL, Liu ZJ, Liu F, Liang LS (2016) Formation mechanism of the graphite-rich protective layer in blast furnace hearths. Int J Miner Metall Mater 23:16\u201324","journal-title":"Int J Miner Metall Mater"},{"key":"674_CR5","doi-asserted-by":"crossref","first-page":"1732","DOI":"10.2355\/isijinternational.47.1732","volume":"47","author":"H SAX\u00e9N","year":"2007","unstructured":"SAX\u00e9N H, Pettersson F (2007) Nonlinear prediction of the hot metal silicon content in the blast furnace. ISIJ Int 47:1732\u20131737","journal-title":"ISIJ Int"},{"key":"674_CR6","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1109\/TIE.2012.2206336","volume":"60","author":"L Jian","year":"2013","unstructured":"Jian L, Gao CH (2013) Binary coding SVMs for the multiclass problem of blast furnace system. IEEE Trans Ind Electron 60:3846\u20133856","journal-title":"IEEE Trans Ind Electron"},{"key":"674_CR7","doi-asserted-by":"crossref","first-page":"979","DOI":"10.2355\/isijinternational.53.979","volume":"53","author":"RS Nick","year":"2013","unstructured":"Nick RS, Tillander A, Jonsson TL (2013) Mathematical model of solid flow behavior in a real dimension blast furnace. ISIJ Int 53:979\u2013987","journal-title":"ISIJ Int"},{"key":"674_CR8","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/0377-2217(94)00304-1","volume":"90","author":"R stermark","year":"1996","unstructured":"stermark R, Sax\u00e9n H (1996) VARMAX-modelling of blast furnace process variables. Eur J Oper Res 90: 85\u2013101","journal-title":"Eur J Oper Res"},{"key":"674_CR9","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.neunet.2016.06.001","volume":"81","author":"JW Cao","year":"2016","unstructured":"Cao JW, Zhang K, Luo M, Yin C, Lai X (2016) Extreme learning machine and adaptive sparse representation for image classification. Neural Netw 81:91\u2013102","journal-title":"Neural Netw"},{"key":"674_CR10","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1016\/j.jprocont.2009.07.006","volume":"19","author":"JS Zeng","year":"2009","unstructured":"Zeng JS, Gao CH (2009) Improvement of identification of blast furnace ironmaking process by outlier detection and missing value imputation. J Proc Control 19:1519\u20131528","journal-title":"J Proc Control"},{"key":"674_CR11","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.2355\/isijinternational.47.1102","volume":"47","author":"SH Luo","year":"2007","unstructured":"Luo SH, Liu XG, Zeng JS (2007), Identification of multi-fractal characteristics of silicon content in blast furnace hot metal. ISIJ Int 47:1102\u20131107","journal-title":"ISIJ Int"},{"key":"674_CR12","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1021\/ie990504+","volume":"39","author":"M Waller","year":"2000","unstructured":"Waller M, Sax\u00e9n H (2000) On the development of predictive models with applications to a metallurgical process. Ind Eng Chem Res 39:982\u2013988","journal-title":"Ind Eng Chem Res"},{"key":"674_CR13","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/srin.201000082","volume":"82","author":"L Jian","year":"2011","unstructured":"Jian L, Gao CH, Xia ZQ(2011) A sliding-window smooth support vector regression model for nonlinear blast furnace system. Steel Res Int 82:169\u2013179","journal-title":"Steel Res Int"},{"key":"674_CR14","doi-asserted-by":"crossref","first-page":"11853","DOI":"10.1016\/j.eswa.2009.04.015","volume":"36","author":"X Tang","year":"2009","unstructured":"Tang X, Zhuang L, Jiang C (2009) Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization. Expert Syst Appl 36:11853\u201311857","journal-title":"Expert Syst Appl"},{"key":"674_CR15","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.2355\/isijinternational.48.1734","volume":"48","author":"JS Zeng","year":"2008","unstructured":"Zeng JS, Liu XG, Gao CH, Luo SH, Jian L (2008) Wiener model identification of blast furnace ironmaking process. ISIJ Int 48:1734\u20131738","journal-title":"ISIJ Int"},{"key":"674_CR16","first-page":"68","volume":"10","author":"DL Zheng","year":"2003","unstructured":"Zheng DL, Liang RX, Zhou Y, Wang Y (2003) A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal, J Univ Sci Technol Beijing 10:68","journal-title":"J Univ Sci Technol Beijing"},{"key":"674_CR17","doi-asserted-by":"crossref","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 70:489\u2013501","journal-title":"Neurocomputing"},{"key":"674_CR18","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"2","author":"GB Huang","year":"2012","unstructured":"Huang GB, Zhou HM, Ding XJ, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 2:513\u2013529","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"},{"key":"674_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-016-0509-z","author":"W Mao","year":"2016","unstructured":"Mao W, Wang J, Xue Z (2016) An ELM-based model with sparse-weighting strategy for sequential data imbalance problem. Int J Mach Learn Cyber. doi: 10.1007\/s13042-016-0509-z","journal-title":"Int J Mach Learn Cyber"},{"key":"674_CR20","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.neucom.2012.08.010","volume":"101","author":"WW Zong","year":"2012","unstructured":"Zong WW, Huang GB, Chen YQ (2012) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229\u2013242","journal-title":"Neurocomputing"},{"key":"674_CR21","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.neucom.2015.04.106","volume":"174","author":"HG Zhang","year":"2016","unstructured":"Zhang HG, Yin YX, Zhang S (2016) An improved ELM algorithm for the measurement of hot metal temperature in blast furnace. Neurocomputing 174:232\u2013237.","journal-title":"Neurocomputing"},{"key":"674_CR22","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"GB Huang","year":"2011","unstructured":"Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cyber 2:107\u2013122","journal-title":"Int J Mach Learn Cyber"},{"key":"674_CR23","doi-asserted-by":"crossref","unstructured":"Zhou XR (2014) Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism, Mathematical Problems in Engineering, Article ID 938548.","DOI":"10.1155\/2014\/938548"},{"key":"674_CR24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/87.974338","volume":"10","author":"RK Pearson","year":"2002","unstructured":"Pearson RK (2002) Outliers in process modeling and identification. IEEE Trans Control Syst Technol 10:55\u201363","journal-title":"IEEE Trans Control Syst Technol"},{"key":"674_CR25","unstructured":"Fletcher R (1981) Practical methods of optimization: vol. 2 constrained optimization. New York"},{"key":"674_CR26","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/BF03014491","volume":"4","author":"BY Sohn","year":"1997","unstructured":"Sohn BY, Kim GB (1997) Detection of outliers in weighted least squares regression. Korean J Comp Appl Math 4:441\u2013452","journal-title":"Korean J Comp Appl Math"},{"key":"674_CR27","volume-title":"Weighted least squares regression diagnostics and its application to robust regression, Doctoral Thesis","author":"BY Sohn","year":"1994","unstructured":"Sohn BY (1994), Weighted least squares regression diagnostics and its application to robust regression, Doctoral Thesis. Dept. of Statistics Korea University, Seoul"},{"key":"674_CR28","first-page":"247","volume":"7","author":"K Limo","year":"1996","unstructured":"Limo K (1996) Robust error measure for supervised neural network learning with outliers. IEEE Trans Neural Netw 7:247\u2013250","journal-title":"IEEE Trans Neural Netw"},{"key":"674_CR29","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.2355\/isijinternational.48.1659","volume":"48","author":"L Jian","year":"2008","unstructured":"Jian L, Gao CH, Li L, Zend JS (2008), Application of least squares support vector machines to predict the silicon content in blast furnace hot metal. ISIJ Int 48:1659\u20131660","journal-title":"ISIJ Int"},{"key":"674_CR30","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1016\/j.neucom.2014.09.022","volume":"151","author":"K Zhang","year":"2015","unstructured":"Zhang K, Luo MX (2015) Outlier-robust extreme learning machine for regression problems. Neurocomputing 151:1519\u20131527","journal-title":"Neurocomputing"},{"key":"674_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2605923","author":"M Ankur","year":"2016","unstructured":"Ankur M, Bikash CP (2016) Bad data detection in the context of leverage point attacks in modern power networks. IEEE Trans Smart Grid. doi: 10.1109\/TSG.2016.2605923","journal-title":"IEEE Trans Smart Grid"},{"key":"674_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2609999","author":"J Cao","year":"2016","unstructured":"Cao J, Wang W, Wang J, Wang R (2016), Excavation equipment recognition based on novel acoustic statistical features. IEEE Trans Cybern. doi: 10.1109\/TCYB.2016.2609999","journal-title":"IEEE Trans Cybern"},{"key":"674_CR33","unstructured":"Bache K, Lichman M (2013) UCI Machine Learning Repository, ( http:\/\/archive.ics.uci . edu\/ml), School of Information and Computer Sciences, University of California, Irvine"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-017-0674-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0674-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0674-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T04:37:59Z","timestamp":1602131879000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-017-0674-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,20]]},"references-count":33,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2018,10]]}},"alternative-id":["674"],"URL":"https:\/\/doi.org\/10.1007\/s13042-017-0674-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,5,20]]}}}