FFT-based deep feature learning method for EEG classification

2021 ◽  
Vol 66 ◽  
pp. 102492
Author(s):  
Mingyang Li ◽  
Wanzhong Chen
Keyword(s):  
Feature Learning ◽  
Learning Method ◽  
Eeg Classification ◽  
Deep Feature ◽  
Deep Feature Learning

scholarly journals An Intelligent Deep Feature Learning Method With Improved Activation Functions for Machine Fault Diagnosis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 1975-1985 ◽  
Author(s):  
Wei You ◽  
Changqing Shen ◽  
Dong Wang ◽  
Liang Chen ◽  
Xingxing Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Learning Method ◽  
Activation Functions ◽  
Machine Fault Diagnosis ◽  
Deep Feature ◽  
Machine Fault ◽  
Deep Feature Learning

scholarly journals Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

2021 ◽  
Vol 13 (8) ◽  
pp. 1455
Author(s):  
Jifang Pei ◽  
Weibo Huo ◽  
Chenwei Wang ◽  
Yulin Huang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Target Recognition ◽  
Feature Learning ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Sar Images ◽  
Single View ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Classification Information

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

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