Analysis of the sports action recognition model based on the LSTM recurrent neural network
With the rapid growth of motion data, the traditional motion recognition algorithm is faced with the problem of insufficient processing ability. To solve this problem, a method based on gradient descent optimization (GDO)–long short-term memory (LSTM) is proposed to meet the needs of sports action r...
Saved in:
| Main Authors: | Chen Ping, Peng Jiangui |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-02-01
|
| Series: | Nonlinear Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nleng-2024-0050 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comparison of the efficiency of zero and first order minimization methods in neural networks
by: E. A. Gubareva, et al.
Published: (2022-12-01) -
Applicability of Small and Low-Cost Magnetic Sensors to Geophysical Exploration
by: Filippo Accomando, et al.
Published: (2024-10-01) -
Stability of Back Propagation Training Algorithm for Neural Networks
by: Baghdad Science Journal
Published: (2012-12-01) -
A Novel Approach for Differential Privacy-Preserving Federated Learning
by: Anis Elgabli, et al.
Published: (2025-01-01) -
Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network
by: Jialin Zhang, et al.
Published: (2025-04-01)