Interpretable Memristive LSTM Network Design for Probabilistic Residential Load Forecasting-Reference-Cited by-同舟云学术

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Interpretable Memristive LSTM Network Design for Probabilistic Residential Load Forecasting

Published:2022-06 Issue:6 Volume:69 Page:2297-2310
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Li Chaojie¹^ORCID,Dong Zhaoyang²,Ding Lan³,Petersen Henry³,Qiu Zihang¹^ORCID,Chen Guo¹^ORCID,Prasad Deo³

Affiliation:

1. School of Electrical Engineering and Telecommunications, University of New South Wales, Kensington, NSW, Australia

2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

3. School of Built Environment, University of New South Wales, Kensington, NSW, Australia

Funder

Australian Research Council (ARC) Research Hub for Integrated Energy Storage Solutions Project

ARC Discovery Project

University of New South Wales (UNSW) Digital Grid Futures Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/9782465/09750856.pdf?arnumber=9750856

Reference36 articles.

1. Memristor-Based Circuit Design for Multilayer Neural Networks

2. A high precision write/read circuits for memristors using digital input/output interfaces

3. Mixed-precision in-memory computing

4. Strategies to Improve the Accuracy of Memristor-Based Convolutional Neural Networks

5. Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine

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