Name
Title | ||
|---|---|---|
Additive Neural Network Based Static and Dynamic Distortion Modeling for Prior-Knowledge-Free Nyquist ADC Characterization |
Abstract | ||
|---|---|---|
This paper presents a prior-knowledge free modeling method for Nyquist ADCs. Current ADC modeling methods mainly base on known circuit implementation and non-idealities, thus hard to recover non-linear static and dynamic distortions. The proposed method adopts an additive neural network with binary inputs to achieve a data driven, prior-knowledge free modeling method. Both static and dynamic disto... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MWSCAS47672.2021.9531763 | 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) |
Keywords | DocType | ISSN |
Training,Additives,Circuits and systems,Simulation,Neural networks,Switches,Distortion | Conference | 1548-3746 |
ISBN | Citations | PageRank |
978-1-6654-2461-5 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danfeng Zhai | 1 | 0 | 0.34 |
| Peizhe Li | 2 | 0 | 0.34 |
| Jiushan Zhang | 3 | 0 | 0.34 |
| Chixiao Chen | 4 | 13 | 5.20 |
| Fan Ye | 5 | 34 | 21.14 |
| Junyan Ren | 6 | 154 | 41.40 |