scholarly journals A novel method based on long short term memory network and discrete-time zeroing neural algorithm for upper-limb continuous estimation using sEMG signals

2021 ◽  
Vol 67 ◽  
pp. 102416
Author(s):  
Yuanyuan Chai ◽  
Keping Liu ◽  
Chunxu Li ◽  
Zhongbo Sun ◽  
Long Jin ◽  
...  
Keyword(s):  
Discrete Time ◽  
Upper Limb ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Novel Method ◽  
Long Short Term Memory ◽  
Continuous Estimation ◽  
Semg Signals

sEMG-based continuous estimation of grasp movements by long-short term memory network

2020 ◽  
Vol 59 ◽  
pp. 101774 ◽  
Author(s):  
Chao Wang ◽  
Weiyu Guo ◽  
Hang Zhang ◽  
Linlin Guo ◽  
Changcheng Huang ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Continuous Estimation

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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