An Efficient Batch-Adjustment Algorithm for Artificial Neural Network Structure Adaptation and Applications to Microwave Modeling
Author:
Affiliation:
1. Faculty of Information Technology, Beijing University of Technology, Beijing, China
2. State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China
Funder
National Natural Science Foundation of China
Scientific Research Project of Beijing Educational Committee
Natural Science Foundation of Jiangsu Province
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9944983/10213341/10130803.pdf?arnumber=10130803
Reference19 articles.
1. Combined LP and Quasi-Newton Methods for Nonlinear $l_1 $ Optimization
2. Automated Multilayer Neural Network Structure Adaptation Method With l₁ Regularization for Microwave Modeling
3. Reliable Computationally Efficient Behavioral Modeling of Microwave Passives Using Deep Learning Surrogates in Confined Domains
4. A Unified Automated Parametric Modeling Algorithm Using Knowledge-Based Neural Network and ${l}_{1}$ Optimization
5. A Method to Select Optimal Deep Neural Network Model for Power Amplifiers
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