{"id":"https://openalex.org/W4365790165","doi":"https://doi.org/10.1109/tcsi.2023.3264616","title":"A New Macromodeling Method Based on Deep Gated Recurrent Unit Regularized With Gaussian Dropout for Nonlinear Circuits","display_name":"A New Macromodeling Method Based on Deep Gated Recurrent Unit Regularized With Gaussian Dropout for Nonlinear Circuits","publication_year":2023,"publication_date":"2023-04-13","ids":{"openalex":"https://openalex.org/W4365790165","doi":"https://doi.org/10.1109/tcsi.2023.3264616"},"language":"en","primary_location":{"id":"doi:10.1109/tcsi.2023.3264616","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsi.2023.3264616","pdf_url":null,"source":{"id":"https://openalex.org/S116977442","display_name":"IEEE Transactions on Circuits and Systems I Regular Papers","issn_l":"1549-8328","issn":["1549-8328","1558-0806"],"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 Circuits and Systems I: Regular Papers","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/A5061330386","display_name":"Amin Faraji","orcid":"https://orcid.org/0000-0001-6139-6190"},"institutions":[{"id":"https://openalex.org/I112536369","display_name":"Yazd University","ror":"https://ror.org/02x99ac45","country_code":"IR","type":"education","lineage":["https://openalex.org/I112536369"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Amin Faraji","raw_affiliation_strings":["Department of Computer Engineering, Yazd University, Yazd, Iran"],"raw_orcid":"https://orcid.org/0000-0001-6139-6190","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Yazd University, Yazd, Iran","institution_ids":["https://openalex.org/I112536369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071856983","display_name":"Sayed Alireza Sadrossadat","orcid":"https://orcid.org/0000-0002-6192-1167"},"institutions":[{"id":"https://openalex.org/I112536369","display_name":"Yazd University","ror":"https://ror.org/02x99ac45","country_code":"IR","type":"education","lineage":["https://openalex.org/I112536369"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Sayed Alireza Sadrossadat","raw_affiliation_strings":["Department of Computer Engineering, Yazd University, Yazd, Iran"],"raw_orcid":"https://orcid.org/0000-0002-6192-1167","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Yazd University, Yazd, Iran","institution_ids":["https://openalex.org/I112536369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004005905","display_name":"Weicong Na","orcid":"https://orcid.org/0000-0001-9775-5124"},"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":"Weicong Na","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9775-5124","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028975396","display_name":"Feng Feng","orcid":"https://orcid.org/0000-0002-3569-8782"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Feng","raw_affiliation_strings":["School of Microelectronics, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-3569-8782","affiliations":[{"raw_affiliation_string":"School of Microelectronics, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100726170","display_name":"Qi\u2010Jun Zhang","orcid":"https://orcid.org/0000-0001-7852-5331"},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Qi-Jun Zhang","raw_affiliation_strings":["Department of Electronics, Carleton University, Ottawa, Canada"],"raw_orcid":"https://orcid.org/0000-0001-7852-5331","affiliations":[{"raw_affiliation_string":"Department of Electronics, Carleton University, Ottawa, Canada","institution_ids":["https://openalex.org/I67031392"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.1358,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.92169016,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"70","issue":"7","first_page":"2904","last_page":"2915"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9972000122070312,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9972000122070312,"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"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9970999956130981,"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/T10558","display_name":"Advancements in Semiconductor Devices and Circuit Design","score":0.9900000095367432,"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":0.6213782429695129},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5758132934570312},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5732146501541138},{"id":"https://openalex.org/keywords/electronic-circuit","display_name":"Electronic circuit","score":0.5478817820549011},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5450830459594727},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4718121290206909},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45916610956192017},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4425826370716095},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42784255743026733},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42359471321105957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3623086214065552},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3542945384979248},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16273432970046997},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09268167614936829}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6213782429695129},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5758132934570312},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5732146501541138},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.5478817820549011},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5450830459594727},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4718121290206909},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45916610956192017},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4425826370716095},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42784255743026733},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42359471321105957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3623086214065552},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3542945384979248},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16273432970046997},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09268167614936829},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsi.2023.3264616","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsi.2023.3264616","pdf_url":null,"source":{"id":"https://openalex.org/S116977442","display_name":"IEEE Transactions on Circuits and Systems I Regular Papers","issn_l":"1549-8328","issn":["1549-8328","1558-0806"],"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 Circuits and Systems I: Regular Papers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4855500653","display_name":null,"funder_award_id":"62271014","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6720261135","display_name":null,"funder_award_id":"62101382","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/W1924770834","https://openalex.org/W2064675550","https://openalex.org/W2077090243","https://openalex.org/W2077560834","https://openalex.org/W2097924325","https://openalex.org/W2112877014","https://openalex.org/W2113764521","https://openalex.org/W2116663670","https://openalex.org/W2118465280","https://openalex.org/W2160219580","https://openalex.org/W2172244621","https://openalex.org/W2294567968","https://openalex.org/W2326051152","https://openalex.org/W2560474429","https://openalex.org/W2591382403","https://openalex.org/W2787037604","https://openalex.org/W2896253122","https://openalex.org/W2901295164","https://openalex.org/W2936869376","https://openalex.org/W2962949994","https://openalex.org/W2964199361","https://openalex.org/W2964347220","https://openalex.org/W2968784654","https://openalex.org/W2972081475","https://openalex.org/W3011675012","https://openalex.org/W3017318748","https://openalex.org/W3023183603","https://openalex.org/W3025117302","https://openalex.org/W3106074051","https://openalex.org/W3119696219","https://openalex.org/W3139016650","https://openalex.org/W3146559208","https://openalex.org/W3158928873","https://openalex.org/W3160755055","https://openalex.org/W3200286837","https://openalex.org/W3212406527","https://openalex.org/W3214924456","https://openalex.org/W4210911084","https://openalex.org/W4211233486","https://openalex.org/W4285286030","https://openalex.org/W4296338953","https://openalex.org/W4313052839","https://openalex.org/W6601425427","https://openalex.org/W6640212811","https://openalex.org/W6696879442"],"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":{"In":[0],"this":[1,135],"paper,":[2],"for":[3,21],"the":[4,7,30,37,42,47,72,115,123,143,152,156,163,169,172,186,189,194,200,203],"first":[5],"time,":[6],"deep":[8,138],"gated":[9],"recurrent":[10,196],"unit":[11],"(Deep":[12],"GRU)":[13],"is":[14,101,132,181],"used":[15],"as":[16],"a":[17,52,126],"new":[18],"macromodeling":[19,99,118,205],"approach":[20],"nonlinear":[22,105,179],"circuits.":[23],"Similar":[24],"to":[25,46,59,64,67,77,84,88,114,141],"Long":[26],"Short-Term":[27],"Memory":[28],"(LSTM),":[29],"GRU":[31,58,124,139],"has":[32],"gating":[33],"units":[34],"that":[35],"control":[36],"information":[38],"flow":[39],"and":[40,90,109,191,202],"makes":[41],"network":[43,198],"less":[44],"prone":[45],"vanishing":[48,89],"gradient":[49,75],"problem.":[50],"Having":[51],"smaller":[53],"number":[54],"of":[55,95,103,171,177,188],"gates":[56,73],"causes":[57],"have":[60,78],"fewer":[61,111],"parameters":[62,112],"compared":[63,113],"LSTM":[65,117],"leading":[66],"better":[68,148],"model":[69],"accuracy.":[70],"Using":[71],"leads":[74],"formulations":[76],"additive":[79],"nature":[80],"which":[81],"helps":[82],"them":[83],"be":[85],"more":[86,107],"resistant":[87],"consequently":[91],"learn":[92],"long":[93],"sequences":[94],"data.":[96],"The":[97],"proposed":[98,157,173,204],"method":[100],"capable":[102],"modeling":[104,176],"circuits":[106,180],"accurately":[108],"using":[110],"conventional":[116,195],"method.":[119],"To":[120,167],"further":[121],"improve":[122],"performance,":[125],"regularization":[127],"technique":[128],"called":[129],"Gaussian":[130],"dropout":[131],"applied":[133],"in":[134,147],"paper":[136],"on":[137],"(GDGRU)":[140],"reduce":[142],"overfitting":[144],"problem":[145],"resulting":[146],"test":[149],"error.":[150],"Additionally,":[151],"models":[153],"obtained":[154],"from":[155],"techniques":[158],"are":[159,207],"remarkably":[160],"faster":[161],"than":[162],"original":[164],"transistor-level":[165],"models.":[166],"verify":[168],"superiority":[170],"method,":[174],"time-domain":[175],"three":[178],"provided.":[182,208],"For":[183],"these":[184],"circuits,":[185],"comparisons":[187],"accuracy":[190],"speed":[192],"between":[193],"neural":[197],"(RNN),":[199],"LSTM,":[201],"methods":[206]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
