Bridging the Gap Between Artificial Neural Networks and Kernel Regressions for Vector-Valued Problems in Microwave Applications
Author:
Affiliation:
1. Department of Electronics and Telecommunications, EMC Group, Politecnico di Torino, Turin, Italy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Radiation
Link
http://xplorestaging.ieee.org/ielx7/22/10144346/10005863.pdf?arnumber=10005863
Reference46 articles.
1. Deep Neural Network Technique for High-Dimensional Microwave Modeling and Applications to Parameter Extraction of Microwave Filters
2. Parameterized Modeling Incorporating MOR-Based Rational Transfer Functions With Neural Networks for Microwave Components
3. Surrogate-Assisted Multistate Tuning-Driven EM Optimization for Microwave Tunable Filter
4. Smart Modeling of Microwave Devices
5. Causal and Passive Parameterization of S-Parameters Using Neural Networks
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