{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113934"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113934","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Input-to-state stable continuous time recurrent neural networks for transient circuit simulation","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_has_math":false,"creators":["Yang, Alan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Raginsky, Maxim","Rosenbaum, Elyse"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:35:55Z","date_published":"2022-04-29T21:35:55Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Engineering"],"languages":["en","eng"],"rights":["Copyright 2021 Alan Yang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113934","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Raginsky, Maxim","Rosenbaum, Elyse"]},{"key":"dc:creator","label":"Author","values":["Yang, Alan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:35:55Z","2021-12","2021-12-10"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Alan Yang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113934"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Alan Yang, accepted the attached license on 2021-12-10 at 10:44.","The student, Alan Yang, submitted this Thesis for approval on 2021-12-10 at 10:53.","This Thesis was approved for publication on 2021-12-10 at 12:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17446 on 2022-04-06 at 17:11:18","Made available in DSpace on 2022-04-29T21:35:55Z (GMT). No. of bitstreams: 2 YANG-THESIS-2021.pdf: 3432861 bytes, checksum: 88ffb72a7b7ef4d91d8426c8ba7ed4e4 (MD5) LICENSE.txt: 4206 bytes, checksum: 1f8aa6221805e4e57a7fede2eca8708d (MD5) Previous issue date: 2021-12-10","This thesis proposes a learning approach for continuous-time recurrent neural network (CTRNN) architectures with zero or one hidden layers that guarantees input-to-state stability (ISS). We propose a model parametrization that guarantees the ISS property with respect to a Lur'e-type ISS Lyapunov function that is learned in conjunction with the model parameters. Our stability constraints impose a physical prior on the learned model, and in some cases improve the convergence of model training. The proposed CTRNN models are used to learn fast-to-simulate transient behavioral models for electronic circuits that can be implemented in the Verilog-A analog behavioral modeling language and simulated in commercial circuit simulators. The proposed CTRNNs are used to learn models of a common-source amplifier and a continuous-time linear equalizer that accurately reproduce the original circuits' behavior when interconnected in circuit configurations not encountered during model training."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Input-to-state stable continuous time recurrent neural networks for transient circuit simulation"]}]}],"canonical_facts":{"dc:contributor":["Raginsky, Maxim","Rosenbaum, Elyse"],"dc:creator":["Yang, Alan"],"dc:date":["2022-04-29T21:35:55Z","2021-12","2021-12-10"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Alan Yang, accepted the attached license on 2021-12-10 at 10:44.","The student, Alan Yang, submitted this Thesis for approval on 2021-12-10 at 10:53.","This Thesis was approved for publication on 2021-12-10 at 12:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17446 on 2022-04-06 at 17:11:18","Made available in DSpace on 2022-04-29T21:35:55Z (GMT). No. of bitstreams: 2 YANG-THESIS-2021.pdf: 3432861 bytes, checksum: 88ffb72a7b7ef4d91d8426c8ba7ed4e4 (MD5) LICENSE.txt: 4206 bytes, checksum: 1f8aa6221805e4e57a7fede2eca8708d (MD5) Previous issue date: 2021-12-10","This thesis proposes a learning approach for continuous-time recurrent neural network (CTRNN) architectures with zero or one hidden layers that guarantees input-to-state stability (ISS). We propose a model parametrization that guarantees the ISS property with respect to a Lur'e-type ISS Lyapunov function that is learned in conjunction with the model parameters. Our stability constraints impose a physical prior on the learned model, and in some cases improve the convergence of model training. The proposed CTRNN models are used to learn fast-to-simulate transient behavioral models for electronic circuits that can be implemented in the Verilog-A analog behavioral modeling language and simulated in commercial circuit simulators. The proposed CTRNNs are used to learn models of a common-source amplifier and a continuous-time linear equalizer that accurately reproduce the original circuits' behavior when interconnected in circuit configurations not encountered during model training."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113934"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Alan Yang"],"dc:subject":["Engineering"],"dc:title":["Input-to-state stable continuous time recurrent neural networks for transient circuit simulation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}