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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)
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Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer Engineering -
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois