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

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Primary Adaptive_Real-Time_Hybrid_Neural_Network-Based_Device-Level_Modeling_for_DC_Traction_HIL_Application.pdf (6.78 MB)

DOI

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

Date

2020-01-01

Author(s)

Tian Liang, Zhen Huang, Venkata Dinavahi

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Abstract

Item Type

http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_970fb48d4fbd8a85

Title

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

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http://creativecommons.org/licenses/by-nc-nd/4.0/

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Subject/Keywords

Behavioral model
Device-level transients
Field 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

Language

en

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Time Period

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Journal Articles (Electrical and Computer Engineering)