Files in this item

FilesDescriptionFormat

application/pdf

application/pdfCHEN-DISSERTATION-2019.pdf (3MB)Restricted to U of Illinois
(no description provided)PDF

Description

Title:Modeling of electrical circuit with recurrent neural networks
Author(s):Chen, Zaichen
Director of Research:Rosenbaum, Elyse
Doctoral Committee Chair(s):Rosenbaum, Elyse
Doctoral Committee Member(s):Hanumolu, Pavan; Raginsky, Maxim; Wong, Martin
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(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.
Issue Date:2019-01-28
Type:Text
URI:http://hdl.handle.net/2142/104961
Rights Information:Copyright 2019 Zaichen Chen
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


This item appears in the following Collection(s)

Item Statistics