A multiple spiking neural network architecture based on fuzzy intervals for anomaly detection: a case study of rail defects-Reference-Cited by-同舟云学术

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A multiple spiking neural network architecture based on fuzzy intervals for anomaly detection: a case study of rail defects

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Author:

Phusakulkajorn Wassamon¹,Hendriks Jurjen¹,Moraal Jan¹,Dollevoet Rolf¹,Li Zili¹,Nunez Alfredo¹

Affiliation:

1. Delft University of Technology,Section of Railway Engineering,Delft,The Netherlands,2628CN

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9882530/9882539/09882864.pdf?arnumber=9882864

Reference42 articles.

1. Classification of Alzheimer’s Disease Using Deep Convolutional Spiking Neural Network

2. Energy efficient ECG classification with spiking neural network

3. A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection

4. Networks of spiking neurons: The third generation of neural network models

5. Integrated condition-based track maintenance planning and crew scheduling of railway networks

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. RFI detection with spiking neural networks;Publications of the Astronomical Society of Australia;2024

2. Artificial intelligence in railway infrastructure: current research, challenges, and future opportunities;Intelligent Transportation Infrastructure;2023

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