{"id":"https://openalex.org/W2978261481","doi":"https://doi.org/10.1109/ijcnn.2019.8852137","title":"Continuous Modeling of Power Plant Performance with Regularized Extreme Learning Machine","display_name":"Continuous Modeling of Power Plant Performance with Regularized Extreme Learning Machine","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978261481","doi":"https://doi.org/10.1109/ijcnn.2019.8852137","mag":"2978261481"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852137","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5100745350","display_name":"Rui Xu","orcid":"https://orcid.org/0000-0003-0516-3629"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Rui Xu","raw_affiliation_strings":["Artificial Intelligence & Machine Learning Laboratory, GE Global Research, Niskayuna, NY, USA"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence & Machine Learning Laboratory, GE Global Research, Niskayuna, NY, USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073342710","display_name":"Weizhong Yan","orcid":"https://orcid.org/0000-0002-7916-8476"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"WeiZhong Yan","raw_affiliation_strings":["Artificial Intelligence & Machine Learning Laboratory, GE Global Research, Niskayuna, NY, USA"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence & Machine Learning Laboratory, GE Global Research, Niskayuna, NY, USA","institution_ids":["https://openalex.org/I4210134512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100745350"],"corresponding_institution_ids":["https://openalex.org/I4210134512"],"apc_list":null,"apc_paid":null,"fwci":0.42,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.71420111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9997000098228455,"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":0.9997000098228455,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9933000206947327,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.6215507984161377},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4544539451599121},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.43732404708862305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41622695326805115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3693985939025879},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16741609573364258},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0752832293510437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6215507984161377},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4544539451599121},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.43732404708862305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41622695326805115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3693985939025879},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16741609573364258},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0752832293510437},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852137","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W976938947","https://openalex.org/W1154478554","https://openalex.org/W1575097427","https://openalex.org/W1904826605","https://openalex.org/W1970833371","https://openalex.org/W1988135700","https://openalex.org/W1989329630","https://openalex.org/W2000454347","https://openalex.org/W2009727399","https://openalex.org/W2053154469","https://openalex.org/W2064769840","https://openalex.org/W2085867967","https://openalex.org/W2088220893","https://openalex.org/W2099419573","https://openalex.org/W2101674911","https://openalex.org/W2109964623","https://openalex.org/W2111072639","https://openalex.org/W2118778444","https://openalex.org/W2129723167","https://openalex.org/W2144219012","https://openalex.org/W2148798622","https://openalex.org/W2153876420","https://openalex.org/W2154852616","https://openalex.org/W2158054309","https://openalex.org/W2160512933","https://openalex.org/W2244109919","https://openalex.org/W2289463038","https://openalex.org/W2343742239","https://openalex.org/W2552178666","https://openalex.org/W2604756720","https://openalex.org/W2735970498","https://openalex.org/W2751686959","https://openalex.org/W2795903893","https://openalex.org/W2913340405","https://openalex.org/W2963232233","https://openalex.org/W3105628676","https://openalex.org/W4235130247","https://openalex.org/W4255466416","https://openalex.org/W6663682518","https://openalex.org/W6682542877"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W2969890106","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W2905251838","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Power":[0],"plant":[1,8,67,138],"modeling":[2],"is":[3,96,101],"critically":[4],"important":[5],"for":[6,92],"power":[7,19,66,137],"operation":[9],"optimization":[10],"and":[11,29,37],"cost":[12],"reduction.":[13],"The":[14,131],"inherently":[15],"nonstationary":[16],"characteristics":[17],"of":[18,109,153],"plants":[20],"raise":[21],"a":[22,70],"big":[23],"challenge":[24],"to":[25,34,39,64,78,80,105,148],"the":[26,31,40,61,93,98,106,123,127,143,154,157,160,172],"learning":[27,32,57],"mechanisms":[28],"require":[30],"algorithms":[33],"adapt":[35],"effectively":[36],"promptly":[38],"continuously":[41],"drifting":[42],"environments.":[43],"In":[44,111],"our":[45],"previous":[46],"study,":[47],"we":[48,90,114],"proposed":[49,94,144],"an":[50],"online":[51,128],"ensemble":[52,129],"regression":[53],"approach,":[54],"with":[55,165],"extreme":[56],"machine":[58],"(ELM)":[59],"as":[60,122],"base":[62,124],"model,":[63],"model":[65,125],"performance":[68,100,152,164],"in":[69,176],"dynamic":[71],"environment,":[72],"which":[73],"can":[74,146],"autonomously":[75],"update":[76],"models":[77],"respond":[79],"environmental":[81],"changes,":[82],"either":[83],"gradual":[84],"or":[85],"abrupt.":[86],"However,":[87],"one":[88],"drawback":[89],"observed":[91],"approach":[95],"that":[97,142],"algorithm":[99,161],"not":[102],"stable":[103,150],"due":[104],"randomness":[107],"nature":[108],"ELMs.":[110],"this":[112,116],"paper,":[113],"address":[115],"issue":[117],"by":[118],"applying":[119],"regularized":[120],"ELM":[121],"within":[126],"framework.":[130],"empirical":[132],"results":[133],"on":[134],"three":[135],"real":[136,177],"data":[139],"sets":[140],"demonstrate":[141],"modification":[145],"lead":[147],"more":[149],"generalization":[151],"algorithm.":[155],"At":[156],"same":[158],"time,":[159],"consistently":[162],"achieves":[163],"mean":[166],"average":[167],"percentage":[168],"error":[169],"less":[170],"than":[171],"required":[173],"1%":[174],"threshold":[175],"field":[178],"operations.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
