Refrigerated Showcase Fault Detection by an Autoencoder with the Adaptive Kernel Size Tuning using Maximum Correntropy Criterion-Reference-Cited by-同舟云学术

Refrigerated Showcase Fault Detection by an Autoencoder with the Adaptive Kernel Size Tuning using Maximum Correntropy Criterion

Published:2022-12-04 Issue: Volume: Page:
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Container-title:2022 IEEE Symposium Series on Computational Intelligence (SSCI)
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Author:

Igarashi Msato¹,Fukuyama Yoshikazu¹,Shimasaki Yuichi²,Osada Yuto²,Murakami Kenya²,Iizaka Tatsuya²

Affiliation:

1. Graduate School of Advanced Mathematical Sciences Meiji University,Tokyo,Japan

2. Adamo Santana, and Tetsuro Matsui, Corporate R&D headquarters Fuji Electric Co., Ltd.,Tokyo,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10022049/10022014/10022112.pdf?arnumber=10022112

Reference21 articles.

1. Refrigerated Showcase Fault Detection by a Pasting based Artificial Neural Networks using Parallel Multi-population Modified Brain Storm optimization and Correntropy

2. Refrigerated showcase fault analysis by a Correntropy based artificial neural network using fast brain swarm optimization;otaka;Proc 7th International Conference on Swarm Intelligence,2019

3. Refrigerated Showcase Fault Detection by an Artificial Neural Network using Correntropy with Improved Adaptive Kernel Size Tuning

4. Daily Peak Load Forecasting by a Correntropy based Artificial Neural Network using an Adaptive Kernel Size Method Considering Outliers

5. Adam: A Method for Stochastic optimization;kingma;3rd International Conference for Learning Representations,2014

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