Enhanced memristor‐based MNNs performance on noisy dataset resulting from memristive stochasticity-Reference-Cited by-同舟云学术

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Enhanced memristor‐based MNNs performance on noisy dataset resulting from memristive stochasticity

Published:2019-07 Issue:5 Volume:13 Page:704-709
ISSN:1751-858X
Container-title:IET Circuits, Devices & Systems
language:en
Short-container-title:IET Circuits, Devices & Systems

Author:

Wu Kechuan¹²,Wang Xiaoping¹²

Affiliation:

1. School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan430074People's Republic of China

2. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhan430074People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cds.2018.5532

Reference34 articles.

1. Deep learning

2. In-Datacenter Performance Analysis of a Tensor Processing Unit

3. Memristor-The missing circuit element

4. The missing memristor found

5. High switching endurance in TaOx memristive devices

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A simple memristive jerk system;IET Circuits, Devices & Systems;2021-03-11

2. Pinched hysteresis loops in non‐linear resonators;IET Circuits, Devices & Systems;2020-12-23

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