{"id":"https://openalex.org/W2035357253","doi":"https://doi.org/10.1109/tnnls.2014.2362555","title":"Two Efficient Twin ELM Methods With Prediction Interval","display_name":"Two Efficient Twin ELM Methods With Prediction Interval","publication_year":2014,"publication_date":"2014-11-20","ids":{"openalex":"https://openalex.org/W2035357253","doi":"https://doi.org/10.1109/tnnls.2014.2362555","mag":"2035357253","pmid":"https://pubmed.ncbi.nlm.nih.gov/25423657"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2014.2362555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2014.2362555","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059512225","display_name":"Kefeng Ning","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kefeng Ning","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343911","display_name":"Min Liu","orcid":"https://orcid.org/0000-0002-7273-0518"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Liu","raw_affiliation_strings":["Department of Thermal Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Thermal Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102897652","display_name":"Mingyu Dong","orcid":"https://orcid.org/0000-0003-3625-2296"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyu Dong","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100752413","display_name":"Cheng Wu","orcid":"https://orcid.org/0000-0002-8611-2665"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Wu","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China","Tsinghua National Laboratory for Information Science and Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua National Laboratory for Information Science and Technology, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100526406","display_name":"WU Zhansong","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"ZhanSong Wu","raw_affiliation_strings":["Department of Thermal Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Thermal Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5059512225"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.227,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.84426413,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"26","issue":"9","first_page":"2058","last_page":"2071"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9776999950408936,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.8762306571006775},{"id":"https://openalex.org/keywords/tikhonov-regularization","display_name":"Tikhonov regularization","score":0.6061246395111084},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5220922231674194},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5148374438285828},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.46527737379074097},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4504741132259369},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43468743562698364},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.42134207487106323},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.41552871465682983},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.37885063886642456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33152860403060913},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18006062507629395},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.10053268074989319}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.8762306571006775},{"id":"https://openalex.org/C152442038","wikidata":"https://www.wikidata.org/wiki/Q2778212","display_name":"Tikhonov regularization","level":3,"score":0.6061246395111084},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5220922231674194},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5148374438285828},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.46527737379074097},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4504741132259369},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43468743562698364},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42134207487106323},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41552871465682983},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.37885063886642456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33152860403060913},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18006062507629395},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.10053268074989319},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2014.2362555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2014.2362555","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:25423657","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/25423657","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G213297304","display_name":null,"funder_award_id":"61104172","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2597643892","display_name":null,"funder_award_id":"61025018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5291517420","display_name":null,"funder_award_id":"60834004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7593094089","display_name":null,"funder_award_id":"61021063","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W13473839","https://openalex.org/W70212145","https://openalex.org/W198087410","https://openalex.org/W1546486946","https://openalex.org/W1663973292","https://openalex.org/W1973560868","https://openalex.org/W1981144549","https://openalex.org/W1988872522","https://openalex.org/W1991420715","https://openalex.org/W1993261349","https://openalex.org/W1993717606","https://openalex.org/W1995845002","https://openalex.org/W2033741689","https://openalex.org/W2039852947","https://openalex.org/W2046813720","https://openalex.org/W2053036794","https://openalex.org/W2056711126","https://openalex.org/W2057683924","https://openalex.org/W2060980900","https://openalex.org/W2068928057","https://openalex.org/W2071460053","https://openalex.org/W2072802808","https://openalex.org/W2091426988","https://openalex.org/W2093115432","https://openalex.org/W2107376597","https://openalex.org/W2111072639","https://openalex.org/W2118775636","https://openalex.org/W2124523231","https://openalex.org/W2129832613","https://openalex.org/W2130606817","https://openalex.org/W2134603844","https://openalex.org/W2136602355","https://openalex.org/W2147243899","https://openalex.org/W2148446997","https://openalex.org/W2158054309","https://openalex.org/W2162706466","https://openalex.org/W2168647006","https://openalex.org/W2170860445","https://openalex.org/W2797532987","https://openalex.org/W2905028700","https://openalex.org/W3120740533","https://openalex.org/W4212863985","https://openalex.org/W4240369198","https://openalex.org/W6602842415","https://openalex.org/W6679935922"],"related_works":["https://openalex.org/W2592311268","https://openalex.org/W2337734184","https://openalex.org/W2388364587","https://openalex.org/W2050033254","https://openalex.org/W2322955667","https://openalex.org/W2373176546","https://openalex.org/W2152224705","https://openalex.org/W2385735574","https://openalex.org/W2057439054","https://openalex.org/W2030398504"],"abstract_inverted_index":{"In":[0,59,78],"the":[1,18,50,53,68,71,121,124,134,141,166,170,177,186,192,202,218,241,252,257,263,299,302],"operational":[2,19],"optimization":[3],"and":[4,14,16,21,33,56,145,191,209,230,270,292],"scheduling":[5],"problems":[6,297],"of":[7,36,45,70,110,123,179,204,215,274,286,301],"actual":[8,294],"industrial":[9,295],"processes,":[10],"such":[11,60],"as":[12,185],"iron":[13],"steel,":[15],"microelectronics,":[17],"indices":[20],"process":[22],"parameters":[23,203],"usually":[24],"need":[25],"to":[26,119,150,200,221,250,255,261],"be":[27,138,237],"predicted.":[28],"However,":[29],"for":[30,67,88,239,266],"some":[31,244],"input":[32],"output":[34,69,126],"variables":[35],"these":[37],"prediction":[38,64,73],"models,":[39],"there":[40],"may":[41],"exist":[42],"a":[43,63,96,213,227],"lot":[44],"uncertainties":[46],"coming":[47],"from":[48,279],"themselves,":[49],"measurement":[51],"error,":[52],"rough":[54],"representation,":[55],"so":[57,271],"on.":[58,272],"cases,":[61],"constructing":[62,89],"interval":[65],"(PI)":[66],"corresponding":[72],"model":[74,125],"is":[75,127,148,198],"very":[76],"necessary.":[77],"this":[79],"paper,":[80],"two":[81,223,293],"twin":[82],"extreme":[83,101,159],"learning":[84,102,143,160],"machine":[85,103,161],"(TELM)":[86],"models":[87],"PIs":[90],"are":[91,116,183,246],"proposed.":[92],"First,":[93],"we":[94,154,211],"propose":[95,155],"regularized":[97],"asymmetric":[98,157,171],"least":[99],"squares":[100],"(RALS-ELM)":[104],"method,":[105],"in":[106,131,140,175,189],"which":[107,176,235],"different":[108],"weights":[109,178,216,253],"its":[111,180],"squared":[112],"error":[113,122,136],"loss":[114],"function":[115,182],"set":[117],"according":[118],"whether":[120],"positive":[128],"or":[129],"negative":[130],"order":[132],"that":[133],"above":[135],"can":[137,236],"differentiated":[139],"parameter":[142],"process,":[144],"Tikhonov":[146],"regularization":[147],"introduced":[149],"reduce":[151],"overfitting.":[152],"Then,":[153],"an":[156,231],"Bayesian":[158,167],"(AB-ELM)":[162],"method":[163,188],"based":[164],"on":[165,207,277],"framework":[168],"with":[169],"Gaussian":[172],"distribution":[173],"(AB-ELM),":[174],"likelihood":[181,196],"determined":[184],"same":[187],"RALS-ELM,":[190],"type":[193],"II":[194],"maximum":[195],"algorithm":[197],"derived":[199],"learn":[201],"AB-ELM.":[205],"Based":[206],"RALS-ELM":[208],"AB-ELM,":[210],"use":[212,262],"pair":[214],"following":[217],"reciprocal":[219],"relationship":[220],"obtain":[222],"nonparallel":[224],"regressors,":[225],"including":[226],"lower-bound":[228],"regressor":[229],"upper-bound":[232],"regressor,":[233],"respectively,":[234],"used":[238],"calculating":[240],"PIs.":[242],"Finally,":[243],"discussions":[245],"given,":[247],"about":[248],"how":[249,260],"adjust":[251],"adaptively":[254],"meet":[256],"desired":[258],"PI,":[259],"proposed":[264,303],"TELMs":[265],"nonlinear":[267],"quantile":[268],"regression,":[269],"Results":[273],"numerical":[275],"comparison":[276],"data":[278],"one":[280],"synthetic":[281],"regression":[282,290,296],"problem,":[283],"three":[284],"University":[285],"California":[287],"Irvine":[288],"benchmark":[289],"problems,":[291],"show":[298],"effectiveness":[300],"models.":[304]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
