Macromodeling of Nonlinear High-Speed Circuits Using Novel Hybrid Bidirectional High-Order Deep Recurrent Neural Network-Reference-Cited by-同舟云学术

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Macromodeling of Nonlinear High-Speed Circuits Using Novel Hybrid Bidirectional High-Order Deep Recurrent Neural Network

Published:2024-08 Issue:8 Volume:71 Page:3740-3753
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Zebhi Saeedeh¹,Sadrossadat Sayed Alireza²^ORCID,Na Weicong³^ORCID,Zhang Qi-Jun⁴^ORCID

Affiliation:

1. Department of Electrical Engineering, Yazd University, Yazd, Iran

2. Department of Computer Engineering, Yazd University, Yazd, Iran

3. Faculty of Information Technology, Beijing University of Technology, Beijing, China

4. Department of Electronics, Carleton University, Ottawa, ON, Canada

Funder

National Natural Science Foundation of China

Beijing Municipal Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8919/10618913/10521787.pdf?arnumber=10521787

Reference37 articles.

1. Soft Fault Diagnosis of Analog Circuits Based on a ResNet With Circuit Spectrum Map

2. Adaptive Event-Triggered SMC for Stochastic Switching Systems With Semi-Markov Process and Application to Boost Converter Circuit Model

3. Experimental Study of Fractional-Order RC Circuit Model Using the Caputo and Caputo-Fabrizio Derivatives

4. Rapid Simulation of Photonic Integrated Circuits Using Verilog-A Compact Models

5. Homogeneous Models of Nonlinear Circuits

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