Adaptive Real-Time Hybrid Neural Network- Based Device-Level Modeling for DC Traction HIL Application

Tian Liang, Zhen Huang, Venkata Dinavahi

doi:doi:10.7939/r3-pckm-de02

This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.

View

Download

Communities and Collections

Electrical and Computer Engineering, Department of / Journal Articles (Electrical and Computer Engineering)

Usage

90 views
172 downloads

Adaptive Real-Time Hybrid Neural Network- Based Device-Level Modeling for DC Traction HIL Application

Author(s) / Creator(s)
- Tian Liang, Zhen Huang, Venkata Dinavahi
Date created

2020-01-01
Subjects / Keywords
- Behavioral model, device-level transients, eld programmable gate array (FPGA), hardware-in-the-loop (HIL), insulated-gate bipolar transistor (IGBT), k-nearest neighbors (kNN), recurrent neural network (RNN), real-time systems.
Type of Item

Article (Published)
DOI

https://doi.org/10.7939/r3-pckm-de02
License

Attribution-NonCommercial-NoDerivatives 4.0 International

Language
- English