{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/104961"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/104961","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Modeling of electrical circuit with recurrent neural networks","abstract":"In this dissertation, a circuit modeling methodology using recurrent neural networks (RNNs) is developed. The methodology covers model structure selection, data generation, training, and model implementation for circuit simulation. Several different RNN structures are investigated and their capabilities in circuit modeling are compared. The stability of RNN in the context of circuit modeling is defined and methods to guarantee stability for some RNN structures are developed. The modeling methodology is supported by test cases showing the accuracy and efficiency of RNN models.","abstract_html":"In this dissertation, a circuit modeling methodology using recurrent neural networks (RNNs) is developed. The methodology covers model structure selection, data generation, training, and model implementation for circuit simulation. Several different RNN structures are investigated and their capabilities in circuit modeling are compared. The stability of RNN in the context of circuit modeling is defined and methods to guarantee stability for some RNN structures are developed. The modeling methodology is supported by test cases showing the accuracy and efficiency of RNN models.","abstract_has_math":false,"creators":["Chen, Zaichen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Rosenbaum, Elyse","Hanumolu, Pavan","Raginsky, Maxim","Wong, Martin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:28:05Z","date_published":"2019-08-23T20:28:05Z","updated_at":"2026-07-22T22:24:44Z","subjects":["circuit modeling","behavioral modeling","nonlinear system identification","recurrent neural network"],"languages":["en"],"rights":["Copyright 2019 Zaichen Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/104961","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rosenbaum, Elyse","Hanumolu, Pavan","Raginsky, Maxim","Wong, Martin"]},{"key":"dc:creator","label":"Author","values":["Chen, Zaichen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:28:05Z","2021-08-24T09:15:10Z","2019-01-28","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["circuit modeling","behavioral modeling","nonlinear system identification","recurrent neural network"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Zaichen Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/104961"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this dissertation, a circuit modeling methodology using recurrent neural networks (RNNs) is developed. The methodology covers model structure selection, data generation, training, and model implementation for circuit simulation. Several different RNN structures are investigated and their capabilities in circuit modeling are compared. The stability of RNN in the context of circuit modeling is defined and methods to guarantee stability for some RNN structures are developed. The modeling methodology is supported by test cases showing the accuracy and efficiency of RNN models.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Zaichen Chen, accepted the attached license on 2019-01-26 at 10:49.","The student, Zaichen Chen, submitted this Dissertation for approval on 2019-01-26 at 10:59.","This Dissertation was approved for publication on 2019-01-28 at 11:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13370 on 2019-08-22 at 15:04:00","Made available in DSpace on 2019-08-23T20:28:05Z (GMT). No. of bitstreams: 2 CHEN-DISSERTATION-2019.pdf: 3183380 bytes, checksum: e403037fa4b4a2ab35fe8cfffe8b3067 (MD5) LICENSE.txt: 4209 bytes, checksum: 43307c59f3dd964421f005507b2e55d0 (MD5) Previous issue date: 2019-01-28","Embargo set by: Seth Robbins for item 112078 Lift date: 2021-08-23T20:28:11Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 112078 Lift date: 2021-08-23T20:29:33Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 112078 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112078 on 2021-08-24T09:15:10Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Modeling of electrical circuit with recurrent neural networks"]}]}],"canonical_facts":{"dc:contributor":["Rosenbaum, Elyse","Hanumolu, Pavan","Raginsky, Maxim","Wong, Martin"],"dc:creator":["Chen, Zaichen"],"dc:date":["2019-08-23T20:28:05Z","2021-08-24T09:15:10Z","2019-01-28","2019-05"],"dc:description":["In this dissertation, a circuit modeling methodology using recurrent neural networks (RNNs) is developed. The methodology covers model structure selection, data generation, training, and model implementation for circuit simulation. Several different RNN structures are investigated and their capabilities in circuit modeling are compared. The stability of RNN in the context of circuit modeling is defined and methods to guarantee stability for some RNN structures are developed. The modeling methodology is supported by test cases showing the accuracy and efficiency of RNN models.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Zaichen Chen, accepted the attached license on 2019-01-26 at 10:49.","The student, Zaichen Chen, submitted this Dissertation for approval on 2019-01-26 at 10:59.","This Dissertation was approved for publication on 2019-01-28 at 11:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13370 on 2019-08-22 at 15:04:00","Made available in DSpace on 2019-08-23T20:28:05Z (GMT). No. of bitstreams: 2 CHEN-DISSERTATION-2019.pdf: 3183380 bytes, checksum: e403037fa4b4a2ab35fe8cfffe8b3067 (MD5) LICENSE.txt: 4209 bytes, checksum: 43307c59f3dd964421f005507b2e55d0 (MD5) Previous issue date: 2019-01-28","Embargo set by: Seth Robbins for item 112078 Lift date: 2021-08-23T20:28:11Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 112078 Lift date: 2021-08-23T20:29:33Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 112078 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112078 on 2021-08-24T09:15:10Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/104961"],"dc:language":["en"],"dc:rights":["Copyright 2019 Zaichen Chen"],"dc:subject":["circuit modeling","behavioral modeling","nonlinear system identification","recurrent neural network"],"dc:title":["Modeling of electrical circuit with recurrent neural networks"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:44Z"}