.png){kind=icon}
Return
Abstract Reservoir Computing
.png){kind=icon}
OA
AI
DOI：10.3390/ai3010012.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Noise of any kind can be an issue when translating results from simulations to the real world. We suddenly have to deal with building tolerances, faulty sensors, or just noisy sensor readings. This is especially evident in systems with many free parameters, such as the ones used in physical reservoir computing. By abstracting away these kinds of noise sources using intervals, we derive a regularized training regime for reservoir computing using sets of possible reservoir states. Numerical simulations are used to show the effectiveness of our approach against different sources of errors that can appear in real-world scenarios and compare them with standard approaches. Our results support the application of interval arithmetics to improve the robustness of mass-spring networks trained in simulations.
Keywords:
reservoir computing
echo state networks
recurrent neural networks
simulation
robustness
Journal
A
IF:
5
Papers:
1.0K
Citations:
941
Organization
Cited Papers
A Preoperative and Postoperative Study of the Accuracy and Value of Electrodiagnosis in Patients with Lumbosacral Disc Herniation.png)
Spine
SpineIF0
PREAI
ShareSaveShareSave
ShareSave
View more