Accelerating FPGA Implementation of Neural Network Controllers via 32-bit Fixed-Point Design for Real-Time Control-Reference-Cited by-同舟云学术

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Accelerating FPGA Implementation of Neural Network Controllers via 32-bit Fixed-Point Design for Real-Time Control

Published:2023-10-12 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
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Author:

Hingu Chanakya¹,Fu Xingang¹,Challoo Rajab²,Lu Jiang³,Yang Xiaokun³,Qingge Letu⁴

Affiliation:

1. University of Nevada, Reno,Dept. of Electrical and Biomedical Engineering,Reno,USA

2. Texas A&M University-Kingsville,Dept. of Electrical Engineering and Computer Science,Kingsville,USA

3. University of Houston-Clear Lake,Dept. of Computer Engineering,Houston,USA

4. North Carolina A&T State University,Dept. of Computer Science,Greensboro,NC,USA

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10315953/10315721/10316098.pdf?arnumber=10316098

Reference18 articles.

1. Implement Optimal Vector Control for LCL-Filter-Based Grid-Connected Converters by Using Recurrent Neural Networks

2. Training Recurrent Neural Networks With the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter

3. DSP Implementation of a Novel Recurrent Neural Network Controller into a TI Solar Microinverter

4. Design Automation of CMOS Amplifiers Using Precomputed Lookup Tables: A Comparative Study of optimization Algorithms

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