{"id":"https://openalex.org/W4315472301","doi":"https://doi.org/10.1109/cdc51059.2022.9992563","title":"Dealing with collinearity in large-scale linear system identification using Bayesian regularization","display_name":"Dealing with collinearity in large-scale linear system identification using Bayesian regularization","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4315472301","doi":"https://doi.org/10.1109/cdc51059.2022.9992563"},"language":"en","primary_location":{"id":"doi:10.1109/cdc51059.2022.9992563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc51059.2022.9992563","pdf_url":null,"source":{"id":"https://openalex.org/S4363607710","display_name":"2022 IEEE 61st Conference on Decision and Control (CDC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 61st Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5064842849","display_name":"Wenqi Cao","orcid":"https://orcid.org/0000-0002-3811-2337"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqi Cao","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Automation,Shanghai,China","Department of Automation, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Automation,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103084695","display_name":"Gianluigi Pillonetto","orcid":"https://orcid.org/0000-0002-1072-3144"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Gianluigi Pillonetto","raw_affiliation_strings":["University of Padova,Department of Information Engineering,Padova,Italy","Department of Information Engineering, University of Padova, Padova, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Padova,Department of Information Engineering,Padova,Italy","institution_ids":["https://openalex.org/I138689650"]},{"raw_affiliation_string":"Department of Information Engineering, University of Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"196","last_page":"202"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.9994999766349792,"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/T11236","display_name":"Control Systems and Identification","score":0.9994999766349792,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9944999814033508,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9939000010490417,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5864384174346924},{"id":"https://openalex.org/keywords/impulse-response","display_name":"Impulse response","score":0.5548626780509949},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5295615792274475},{"id":"https://openalex.org/keywords/collinearity","display_name":"Collinearity","score":0.5082302093505859},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.45631512999534607},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45092931389808655},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4449404180049896},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.43280819058418274},{"id":"https://openalex.org/keywords/linear-system","display_name":"Linear system","score":0.4297703206539154},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4262658655643463},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.41651085019111633},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3937612771987915},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3567659258842468},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2545168995857239},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22973328828811646}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5864384174346924},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.5548626780509949},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5295615792274475},{"id":"https://openalex.org/C106192678","wikidata":"https://www.wikidata.org/wiki/Q1419761","display_name":"Collinearity","level":2,"score":0.5082302093505859},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.45631512999534607},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45092931389808655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4449404180049896},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.43280819058418274},{"id":"https://openalex.org/C6802819","wikidata":"https://www.wikidata.org/wiki/Q1072174","display_name":"Linear system","level":2,"score":0.4297703206539154},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4262658655643463},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.41651085019111633},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3937612771987915},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3567659258842468},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2545168995857239},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22973328828811646},{"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/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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cdc51059.2022.9992563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc51059.2022.9992563","pdf_url":null,"source":{"id":"https://openalex.org/S4363607710","display_name":"2022 IEEE 61st Conference on Decision and Control (CDC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 61st Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},{"id":"pmh:oai:www.research.unipd.it:11577/3470525","is_oa":false,"landing_page_url":"https://hdl.handle.net/11577/3470525","pdf_url":null,"source":{"id":"https://openalex.org/S4306402547","display_name":"Padua Research Archive (University of Padova)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W178056938","https://openalex.org/W296863324","https://openalex.org/W1862263964","https://openalex.org/W1965292618","https://openalex.org/W1968128579","https://openalex.org/W1976310086","https://openalex.org/W1976446368","https://openalex.org/W1978282180","https://openalex.org/W2006681603","https://openalex.org/W2021065610","https://openalex.org/W2030399903","https://openalex.org/W2037479549","https://openalex.org/W2069840943","https://openalex.org/W2080407621","https://openalex.org/W2092766760","https://openalex.org/W2101517901","https://openalex.org/W2107861998","https://openalex.org/W2113146118","https://openalex.org/W2130416410","https://openalex.org/W2142635246","https://openalex.org/W2159660986","https://openalex.org/W2159929956","https://openalex.org/W2164547844","https://openalex.org/W2171033594","https://openalex.org/W2171853113","https://openalex.org/W2275541611","https://openalex.org/W2529058332","https://openalex.org/W2741075329","https://openalex.org/W2769132152","https://openalex.org/W2951716158","https://openalex.org/W2963721911","https://openalex.org/W2963819503","https://openalex.org/W2992870756","https://openalex.org/W3095194325","https://openalex.org/W3101085072","https://openalex.org/W3107003040","https://openalex.org/W3155777146","https://openalex.org/W3161898171","https://openalex.org/W3203127395","https://openalex.org/W4211049957","https://openalex.org/W4213251304","https://openalex.org/W4226140776","https://openalex.org/W4285265010"],"related_works":["https://openalex.org/W2159670039","https://openalex.org/W4226120311","https://openalex.org/W3016530894","https://openalex.org/W2058928070","https://openalex.org/W4233429336","https://openalex.org/W4235291650","https://openalex.org/W3196914014","https://openalex.org/W4256422528","https://openalex.org/W2140731556","https://openalex.org/W214570580"],"abstract_inverted_index":{"We":[0,54,93],"consider":[1],"the":[2,15,26,41,69,90,113,116,134,138,141,146,155,158,166],"identification":[3],"of":[4,17,43,71,89,115,125,133,140,143,157,162],"large-scale":[5],"linear":[6],"and":[7,48,107,168],"stable":[8,77],"dynamic":[9],"systems":[10,38],"whose":[11],"outputs":[12],"may":[13,24,171],"be":[14,52,172],"result":[16],"many":[18,44],"correlated":[19],"inputs.":[20,148],"Hence,":[21],"severe":[22],"ill-conditioning":[23],"affect":[25],"estimation":[27],"performance.This":[28],"is":[29,66,80,108,120],"a":[30,56,72,96,123],"scenario":[31],"often":[32],"arising":[33],"when":[34],"modeling":[35],"complex":[36],"physical":[37],"given":[39],"by":[40],"interconnection":[42],"sub-units":[45],"where":[46,62,160],"feedback":[47],"algebraic":[49],"loops":[50],"can":[51],"encountered.":[53],"develop":[55],"strategy":[57],"based":[58,121],"on":[59,85,122,137],"Bayesian":[60],"regularization":[61],"any":[63],"impulse":[64,91,117,163],"response":[65],"modeled":[67],"as":[68],"realization":[70],"zero-mean":[73],"Gaussian":[74],"process.":[75],"The":[76],"spline":[78],"covariance":[79],"used":[81],"to":[82,110,153],"include":[83],"information":[84],"smooth":[86],"exponential":[87],"decay":[88],"responses.":[92,118],"then":[94],"design":[95],"new":[97],"Markov":[98],"chain":[99],"Monte":[100],"Carlo":[101],"scheme":[102],"that":[103],"deals":[104],"with":[105],"collinearity":[106,144],"able":[109],"efficiently":[111],"reconstruct":[112],"posterior":[114],"It":[119],"variation":[124],"Gibbs":[126],"sampling":[127],"which":[128],"updates":[129],"possibly":[130],"overlapping":[131],"blocks":[132],"parameter":[135],"space":[136],"basis":[139],"level":[142],"affecting":[145],"different":[147],"Numerical":[149],"experiments":[150],"are":[151],"included":[152],"test":[154],"goodness":[156],"approach":[159],"hundreds":[161],"responses":[164],"form":[165],"system":[167],"inputs":[169],"correlation":[170],"very":[173],"high.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
