Modeling of electrical circuit with recurrent neural networks

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Chen, Zaichen

Contributors dc:contributor

• Rosenbaum, Elyse
• Hanumolu, Pavan
• Raginsky, Maxim
• Wong, Martin

• circuit modeling
• behavioral modeling
• nonlinear system identification
• recurrent neural network

Statement dc:rights

• Copyright 2019 Zaichen Chen

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/104961

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/104961