Comparison of initial learning algorithms for long short-term memory method on real-time respiratory signal prediction
AimThis study aimed to examine the effect of the weight initializers on the respiratory signal prediction performance using the long short-term memory (LSTM) model.MethodsRespiratory signals collected with the CyberKnife Synchrony device during 304 breathing motion traces were used in this study. Th...
Main Authors: | Wenzheng Sun, Jun Dang, Lei Zhang, Qichun Wei |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-01-01
|
Series: | Frontiers in Oncology |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1101225/full |
Similar Items
-
Short term unpredictability of high Reynolds number turbulence - rough dependence on initial data
by: Zaichun Feng, et al.
Published: (2020-10-01) -
ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization
by: Qian SUN, et al.
Published: (2018-01-01) -
BRNN-LSTM for Initial Access in Millimeter Wave Communications
by: Adel Aldalbahi, et al.
Published: (2021-06-01) -
Fractional Order Models Are Doubly Infinite Dimensional Models and thus of Infinite Memory: Consequences on Initialization and Some Solutions
by: Jocelyn Sabatier
Published: (2021-06-01) -
A Novel Initialization Technique for Decadal Climate Predictions
by: Danila Volpi, et al.
Published: (2021-06-01)