Force Control of SMA Springs Using RNNs
Shape memory alloys (SMAs), generally composed of nickel-titanium, have unique properties that enable them to transition from the martensite phase to the austenite phase when heated. During this transformation, SMAs can shorten and revert to their original shape, which makes them valuable as actuato...
Saved in:
| Main Authors: | Ahmet Atasoy, Mehmed Ozkan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11005977/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep learning in time series forecasting with transformer models and RNNs
by: Rogerio Pereira dos Santos, et al.
Published: (2025-07-01) -
A New Varying-Factor Finite-Time Recurrent Neural Network to Solve the Time-Varying Sylvester Equation Online
by: Haoming Tan, et al.
Published: (2024-12-01) -
Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
by: Rana Tabassum, et al.
Published: (2024-09-01) -
Pengenalan Ucapan Bahasa Indonesia Menggunakan MFCC dan Recurrent Neural Network
by: Panggih Tridarma, et al.
Published: (2020-11-01) -
Long Short-Term Memory Approach for Coronavirus Disease Predicti
by: Omar Ibrahim Obaid, et al.
Published: (2020-12-01)