Modeling of electrical circuit with recurrent neural networks
Chen, Zaichen
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https://hdl.handle.net/2142/104961
Description
Title
Modeling of electrical circuit with recurrent neural networks
Author(s)
Chen, Zaichen
Issue Date
2019-01-28
Director of Research (if dissertation) or Advisor (if thesis)
Rosenbaum, Elyse
Doctoral Committee Chair(s)
Rosenbaum, Elyse
Committee Member(s)
Hanumolu, Pavan
Raginsky, Maxim
Wong, Martin
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
circuit modeling
behavioral modeling
nonlinear system identification
recurrent neural network
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.
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