Memristor-based LSTM network with in situ training and its applications-Reference-Cited by-同舟云学术

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Memristor-based LSTM network with in situ training and its applications

Published:2020-11 Issue: Volume:131 Page:300-311
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Liu Xiaoyang,Zeng Zhigang,Wunsch II Donald C.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Foundation for Innovative Research Groups of Hubei Province of China

111 Project on Computational Intelligence and Intelligent Control

Missouri University of Science and Technology Mary K. Finley Endowment and Intelligent Systems Center, USA

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference71 articles.

1. Adam, K., Smagulova, K., & James, A. P. (2018). Memristive LSTM network hardware architecture for time-series predictive modeling problems. In Proceedings of the IEEE asia pacific conference on circuits and systems (pp. 459–462).

2. Adam, K., Smagulova, K., Krestinskaya, O., & James, A. P. (2018). Wafer quality inspection using memristive LSTM, ANN, DNN and HTM. In Proceedings of the IEEE electrical design of advanced packaging and systems symposium (pp. 1–3).

3. Memristor bridge synapse-based neural network and its learning;Adhikari;IEEE Transactions on Neural Networks and Learning Systems,2012

4. Agarwal, S., Plimpton, S. J., Hughart, D. R., Hsia, A. H., Richter, I., & Cox, J. A., et al. (2016). Resistive memory device requirements for a neural algorithm accelerator. In Proceedings of the international joint conference on neural networks (pp. 929–938).

5. Pattern classification by memristive crossbar circuits using ex situ and in situ training;Alibart;Nature Communications,2013

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