{"id":"https://openalex.org/W2895109358","doi":"https://doi.org/10.1109/tase.2018.2865663","title":"TL-GDBN: Growing Deep Belief Network With Transfer Learning","display_name":"TL-GDBN: Growing Deep Belief Network With Transfer Learning","publication_year":2018,"publication_date":"2018-10-02","ids":{"openalex":"https://openalex.org/W2895109358","doi":"https://doi.org/10.1109/tase.2018.2865663","mag":"2895109358"},"language":"en","primary_location":{"id":"doi:10.1109/tase.2018.2865663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2018.2865663","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"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 Automation Science and Engineering","raw_type":"journal-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/A5108028771","display_name":"Gongming Wang","orcid":"https://orcid.org/0000-0002-8124-2890"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"GongMing Wang","raw_affiliation_strings":["Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China","institution_ids":["https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055432844","display_name":"Junfei Qiao","orcid":"https://orcid.org/0000-0002-1707-6074"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JunFei Qiao","raw_affiliation_strings":["Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China","institution_ids":["https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057935426","display_name":"Jing Bi","orcid":"https://orcid.org/0000-0002-4610-0141"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Bi","raw_affiliation_strings":["Faculty of Information Technology, School of Software Engineering, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, School of Software Engineering, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100646319","display_name":"Wenjing Li","orcid":"https://orcid.org/0000-0002-3218-0515"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"WenJing Li","raw_affiliation_strings":["Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China","institution_ids":["https://openalex.org/I4210100255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081318069","display_name":"MengChu Zhou","orcid":"https://orcid.org/0000-0002-5408-8752"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"MengChu Zhou","raw_affiliation_strings":["Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5108028771"],"corresponding_institution_ids":["https://openalex.org/I4210100255"],"apc_list":null,"apc_paid":null,"fwci":10.424,"has_fulltext":false,"cited_by_count":132,"citation_normalized_percentile":{"value":0.9849868,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"16","issue":"2","first_page":"874","last_page":"885"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9987999796867371,"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":0.9987999796867371,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973999857902527,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.7424982786178589},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.717641294002533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7116456031799316},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6389633417129517},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6127066612243652},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5582119822502136},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5463429689407349},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5395033359527588},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.5199221968650818},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5070536136627197},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4594472646713257},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44390150904655457},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11310365796089172}],"concepts":[{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.7424982786178589},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.717641294002533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7116456031799316},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6389633417129517},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6127066612243652},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5582119822502136},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5463429689407349},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5395033359527588},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5199221968650818},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5070536136627197},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4594472646713257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44390150904655457},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11310365796089172},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tase.2018.2865663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2018.2865663","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"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 Automation Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8700000047683716,"id":"https://metadata.un.org/sdg/6","display_name":"Clean water and sanitation"}],"awards":[{"id":"https://openalex.org/G4257931717","display_name":null,"funder_award_id":"2018ZX07111005","funder_id":"https://openalex.org/F4320329860","funder_display_name":"National Science and Technology Major Project"},{"id":"https://openalex.org/G5654010599","display_name":null,"funder_award_id":"61703011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6199226769","display_name":null,"funder_award_id":"61802015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6326048529","display_name":null,"funder_award_id":"61603009","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"},{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W222543348","https://openalex.org/W756011849","https://openalex.org/W1661947036","https://openalex.org/W1705374184","https://openalex.org/W1912359413","https://openalex.org/W1965558264","https://openalex.org/W1984165601","https://openalex.org/W1986614398","https://openalex.org/W1991482489","https://openalex.org/W2006792461","https://openalex.org/W2019779527","https://openalex.org/W2044100505","https://openalex.org/W2090545185","https://openalex.org/W2097381042","https://openalex.org/W2100664256","https://openalex.org/W2134563198","https://openalex.org/W2134961275","https://openalex.org/W2136922672","https://openalex.org/W2145889472","https://openalex.org/W2147179108","https://openalex.org/W2160389119","https://openalex.org/W2163345210","https://openalex.org/W2163922914","https://openalex.org/W2165698076","https://openalex.org/W2170262723","https://openalex.org/W2253429366","https://openalex.org/W2263928265","https://openalex.org/W2292254049","https://openalex.org/W2314582146","https://openalex.org/W2335277946","https://openalex.org/W2397078533","https://openalex.org/W2418674135","https://openalex.org/W2540224322","https://openalex.org/W2595311085","https://openalex.org/W2620915497","https://openalex.org/W2736761179","https://openalex.org/W2755335499","https://openalex.org/W2766541071","https://openalex.org/W2789527575","https://openalex.org/W2919115771","https://openalex.org/W2963628712","https://openalex.org/W4231109964","https://openalex.org/W6674600207","https://openalex.org/W6679809621","https://openalex.org/W6717190812"],"related_works":["https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W3192840557","https://openalex.org/W2951211570","https://openalex.org/W2585432886","https://openalex.org/W2165991108","https://openalex.org/W4375928479","https://openalex.org/W3167935049","https://openalex.org/W3082895349","https://openalex.org/W3023427754"],"abstract_inverted_index":{"A":[0],"deep":[1,215],"belief":[2,216],"network":[3,217],"(DBN)":[4,218],"is":[5,19,40,67,111,153,157],"effective":[6],"to":[7,21,42,83,92,134,191,196,207,221,241,269,289,308],"create":[8],"a":[9,33,59,102,165,204,213,233,266,286,301],"powerful":[10],"generative":[11],"model":[12,56,230,284],"by":[13,125,200,260],"using":[14,126],"training":[15,198,224],"data.":[16],"However,":[17],"it":[18,175,303],"difficult":[20],"fast":[22],"determine":[23,227],"its":[24,45,51,246],"optimal":[25,229],"structure":[26,46,62,104,186],"given":[27],"specific":[28],"applications.":[29],"In":[30],"this":[31],"paper,":[32],"growing":[34,103,214,251],"DBN":[35,61,239,252],"with":[36,63,219,253,291,297],"transfer":[37,84],"learning":[38,52,181,193,258],"(TL-GDBN)":[39,255],"proposed":[41,250,282],"automatically":[43],"decide":[44],"size,":[47],"which":[48,99,136],"can":[49,100,137,304],"accelerate":[50],"process":[53,235,259],"and":[54,69,72,96,164,183,226,236],"improve":[55,197,222],"accuracy.":[57],"First,":[58],"basic":[60],"single":[64],"hidden":[65,97],"layer":[66],"initialized":[68],"then":[70],"pretrained,":[71],"the":[73,85,88,106,114,147,151,223,228,257,263,281,295],"learned":[74,89],"weight":[75,90,115],"parameters":[76,91,116],"are":[77,122],"frozen.":[78],"Second,":[79],"TL-GDBN":[80,121,152,156,283],"uses":[81],"TL":[82,220,254],"knowledge":[86,202,264],"from":[87,118,132,203,265],"newly":[93],"added":[94],"neurons":[95],"layers,":[98],"achieve":[101],"until":[105],"stopping":[107],"criterion":[108],"for":[109,245],"pretraining":[110,119],"satisfied.":[112],"Third,":[113],"derived":[117],"of":[120,141,150],"further":[123],"fine-tuned":[124],"layer-by-layer":[127],"partial":[128],"least":[129],"square":[130],"regression":[131],"top":[133],"bottom,":[135],"avoid":[138],"many":[139],"problems":[140],"traditional":[142],"backpropagation":[143],"algorithm-based":[144],"fine-tuning.":[145],"Moreover,":[146],"convergence":[148],"analysis":[149],"presented.":[154],"Finally,":[155],"tested":[158],"on":[159],"two":[160],"benchmark":[161],"data":[162],"sets":[163],"practical":[166],"wastewater":[167],"treatment":[168],"system.":[169],"The":[170,249,276],"simulation":[171],"results":[172,278],"show":[173,279],"that":[174,280],"has":[176,285],"better":[177],"modeling":[178],"performance,":[179],"faster":[180],"speed,":[182],"more":[184],"robust":[185],"than":[187],"existing":[188],"models.":[189],"Note":[190],"Practitioners-Transfer":[192],"(TL)":[194],"aims":[195],"effectiveness":[199,225],"transferring":[201,262],"source":[205,267],"domain":[206,268],"target":[208],"domain.":[209],"This":[210],"paper":[211],"presents":[212],"size.":[231],"Facing":[232],"complex":[234,292],"real-world":[237],"workflow,":[238],"tends":[240],"require":[242],"long":[243],"time":[244],"successful":[247],"training.":[248],"accelerates":[256],"instantaneously":[261],"each":[270],"new":[271],"deeper":[272],"or":[273],"wider":[274],"substructure.":[275],"experimental":[277],"great":[287],"potential":[288],"deal":[290],"system,":[293],"especially":[294],"systems":[296],"high":[298],"nonlinearity.":[299],"As":[300],"result,":[302],"be":[305],"readily":[306],"applicable":[307],"some":[309],"industrial":[310],"nonlinear":[311],"systems.":[312]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":32},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":33},{"year":2019,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}