A Hybrid Approach Based on Recurrent Neural Network for Macromodeling of Nonlinear Electronic Circuits
Author:
Affiliation:
1. Department of Computer Engineering, Yazd University, Yazd, Iran
2. Department of Electrical Engineering, Polytechnique Montreal, Montreal, Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09968253.pdf?arnumber=9968253
Reference59 articles.
1. Behavioral modeling of power amplifier with long term memory effects using recurrent neural networks
2. A new macromodeling approach for nonlinear microwave circuits based on recurrent neural networks
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4. A New Training Approach for Robust Recurrent Neural-Network Modeling of Nonlinear Circuits
5. Kriging bootstrapped neural network training for fast and accurate process variation analysis
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1. Macromodeling of Nonlinear High-Speed Circuits Using Novel Hybrid Bidirectional High-Order Deep Recurrent Neural Network;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-08
2. Multi-objective neural network modeling and applications to microwave power amplifiers;Microelectronics Journal;2024-07
3. Hybrid Batch-Normalized Deep Feedforward Neural Network Incorporating Polynomial Regression for High-Dimensional Microwave Modeling;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-03
4. DNN-Based Optimization to Significantly Speed Up and Increase the Accuracy of Electronic Circuit Design;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-03
5. Nonlinear Circuit Macromodeling Using New Heterogeneous-Layered Deep Clockwork Recurrent Neural Network;IEEE Access;2024
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