Input-to-state stable continuous time recurrent neural networks for transient circuit simulation
Yang, Alan
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https://hdl.handle.net/2142/113934
Description
Title
Input-to-state stable continuous time recurrent neural networks for transient circuit simulation
Author(s)
Yang, Alan
Issue Date
2021-12-10
Director of Research (if dissertation) or Advisor (if thesis)
Rosenbaum, Elyse
Raginsky, Maxim
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2022-04-29T21:35:55Z
Keyword(s)
Engineering
Language
eng
Abstract
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.
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