A particle filter and long short term memory fusion algorithm for failure prognostic of proton exchange membrane fuel cells

2017 ◽  
Author(s):  
Chunchun Yang ◽  
Zhiheng Li ◽  
Bin Liang ◽  
Weining Lu ◽  
Xueqian Wang ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Particle Filter ◽  
Proton Exchange Membrane ◽  
Short Term Memory ◽  
Proton Exchange ◽  
Short Term ◽  
Fusion Algorithm ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Exchange Membrane

Data-based short-term prognostics for proton exchange membrane fuel cells

2017 ◽  
Vol 42 (32) ◽  
pp. 20791-20808 ◽  
Author(s):  
Hao Liu ◽  
Jian Chen ◽  
Ming Hou ◽  
Zhigang Shao ◽  
Hongye Su
Keyword(s):  
Fuel Cells ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Short Term ◽  
Exchange Membrane

scholarly journals Long Short-Term Memory Networks Based on Particle Filter for Object Tracking

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 216245-216258
Author(s):  
Yanli Liu ◽  
Jingjing Cheng ◽  
Heng Zhang ◽  
Hang Zou ◽  
Naixue Xiong
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals A Robust Terrain Aided Navigation Using the Rao-Blackwellized Particle Filter Trained by Long Short-Term Memory Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2886 ◽  
Author(s):  
Jungshin Lee ◽  
Hyochoong Bang
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Time Series Data ◽  
Short Term Memory ◽  
Measurement Model ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Elevation Model

Terrain-aided navigation (TAN) is a technology that estimates the position of the vehicle by comparing the altitude measured by an altimeter and height from the digital elevation model (DEM). The particle filter (PF)-based TAN has been commonly used to obtain stable real-time navigation solutions in cases where the unmanned aerial vehicle (UAV) operates at a high altitude. Even though TAN performs well on rough and unique terrains, its performance degrades in flat and repetitive terrains. In particular, in the case of PF-based TAN, there has been no verified technique for deciding its terrain validity. Therefore, this study designed a Rao-Blackwellized PF (RBPF)-based TAN, used long short-term memory (LSTM) networks to endure flat and repetitive terrains, and trained the noise covariances and measurement model of RBPF. LSTM is a modified recurrent neural network (RNN), which is an artificial neural network that recognizes patterns from time series data. Using this, this study tuned the noise covariances and measurement model of RBPF to minimize the navigation errors in various flight trajectories. This paper designed a TAN algorithm based on combining RBPF and LSTM and confirmed that it can enable a more precise navigation performance than conventional RBPF based TAN through simulations.

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