{"id":"https://openalex.org/W3186555519","doi":"https://doi.org/10.23919/acc50511.2021.9483103","title":"Handling Noisy Data in Machine Learning Modeling and Predictive Control of Nonlinear Processes","display_name":"Handling Noisy Data in Machine Learning Modeling and Predictive Control of Nonlinear Processes","publication_year":2021,"publication_date":"2021-05-25","ids":{"openalex":"https://openalex.org/W3186555519","doi":"https://doi.org/10.23919/acc50511.2021.9483103","mag":"3186555519"},"language":"en","primary_location":{"id":"doi:10.23919/acc50511.2021.9483103","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc50511.2021.9483103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 American Control Conference (ACC)","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/A5015091824","display_name":"Zhe Wu","orcid":"https://orcid.org/0000-0002-2923-149X"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhe Wu","raw_affiliation_strings":["Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013295212","display_name":"David Rinc\u00f3n","orcid":"https://orcid.org/0000-0001-7904-2710"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Rincon","raw_affiliation_strings":["Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102017787","display_name":"Junwei Luo","orcid":"https://orcid.org/0009-0007-2609-629X"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junwei Luo","raw_affiliation_strings":["Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002367171","display_name":"Panagiotis D. Christofides","orcid":"https://orcid.org/0000-0002-8772-4348"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Panagiotis D. Christofides","raw_affiliation_strings":["Department of Chemical and Biomolecular Engineering and the Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Chemical and Biomolecular Engineering and the Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5015091824"],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":0.5493,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.65363609,"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":"3345","last_page":"3351"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9955999851226807,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.7722880840301514},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7255830764770508},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.6633919477462769},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6014115810394287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5554606318473816},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5251973867416382},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4869333505630493},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4857178330421448},{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"Model predictive control","score":0.44974827766418457},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4394959509372711},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4279378056526184},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4168933033943176},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.31600844860076904},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.14246052503585815}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7722880840301514},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7255830764770508},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6633919477462769},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6014115810394287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5554606318473816},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5251973867416382},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4869333505630493},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4857178330421448},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.44974827766418457},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4394959509372711},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4279378056526184},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4168933033943176},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.31600844860076904},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.14246052503585815},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/acc50511.2021.9483103","is_oa":false,"landing_page_url":"https://doi.org/10.23919/acc50511.2021.9483103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 American Control Conference (ACC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1904365287","https://openalex.org/W1964357740","https://openalex.org/W1971544913","https://openalex.org/W2018291128","https://openalex.org/W2061200912","https://openalex.org/W2095705004","https://openalex.org/W2109606373","https://openalex.org/W2123871098","https://openalex.org/W2146014298","https://openalex.org/W2212703438","https://openalex.org/W2278773995","https://openalex.org/W2316102489","https://openalex.org/W2788043262","https://openalex.org/W2883194111","https://openalex.org/W2938661119","https://openalex.org/W2947592369","https://openalex.org/W2962821278","https://openalex.org/W2963266340","https://openalex.org/W2963602521","https://openalex.org/W2964059111","https://openalex.org/W2972105067","https://openalex.org/W3015977716","https://openalex.org/W3048643786","https://openalex.org/W6617145748","https://openalex.org/W6640036494","https://openalex.org/W6674330103","https://openalex.org/W6688167117","https://openalex.org/W6761402254"],"related_works":["https://openalex.org/W4298017035","https://openalex.org/W3128220493","https://openalex.org/W2792147139","https://openalex.org/W3110700750","https://openalex.org/W2998675825","https://openalex.org/W4226354336","https://openalex.org/W4394636190","https://openalex.org/W2736804899","https://openalex.org/W2897443685","https://openalex.org/W4307654087"],"abstract_inverted_index":{"Long":[0],"short-term":[1],"memory":[2],"(LSTM)":[3],"networks,":[4,11],"as":[5,53],"one":[6],"type":[7],"of":[8,35,105],"recurrent":[9],"neural":[10],"has":[12],"been":[13],"widely":[14],"utilized":[15],"to":[16,77,118,137,141,151,163],"model":[17,178],"nonlinear":[18,36,82],"dynamic":[19,84],"systems":[20],"from":[21,49,92],"time-series":[22],"process":[23,83,154],"operational":[24],"data.":[25,143],"This":[26],"work":[27],"focuses":[28],"on":[29],"LSTM":[30,73,108,121,130,147],"modeling":[31],"and":[32,56,68,99,172],"predictive":[33,179],"control":[34],"processes":[37],"using":[38,132],"a":[39,63,89,101,152,176],"noisy":[40,90,97,125,142],"training":[41,103],"data":[42],"set,":[43],"where":[44],"the":[45,71,79,106,139],"noise":[46,162],"can":[47],"stem":[48],"different":[50],"sources,":[51],"such":[52],"sensor":[54],"variability":[55],"common":[57],"plant":[58],"variance.":[59],"We":[60],"first":[61],"consider":[62,88],"dataset":[64,91],"with":[65,124,156],"Gaussian":[66,115],"noise,":[67],"demonstrate":[69,100,164],"that":[70],"standard":[72,107],"network":[74,109,131],"is":[75,149],"able":[76],"capture":[78],"underlying":[80],"(nominal)":[81],"behavior.":[85],"Then,":[86],"we":[87,127],"industrial":[93,161],"operation":[94,174],"(i.e.,":[95],"non-Gaussian":[96],"data),":[98],"poor":[102],"performance":[104],"despite":[110],"its":[111,165],"denoising":[112],"capability":[113],"for":[114],"noise.":[116],"Therefore,":[117],"train":[119],"an":[120,129],"more":[122],"efficiently":[123],"data,":[126],"propose":[128],"Monte":[133],"Carlo":[134],"dropout":[135,146],"method":[136,148],"reduce":[138],"overfitting":[140],"The":[144],"proposed":[145],"applied":[150],"chemical":[153],"example":[155],"state":[157],"measurements":[158],"corrupted":[159],"by":[160],"improved":[166],"prediction":[167],"accuracy":[168],"in":[169],"both":[170],"open-":[171],"closed-loop":[173],"under":[175],"Lyapunov-based":[177],"controller.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
