An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis
Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of...
Saved in:
| Main Authors: | Syed Saad Azhar Ali, Muhammad Moinuddin, Kamran Raza, Syed Hasan Adil |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/850189 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Output Feedback Adaptive Dynamic Surface Control of Permanent Magnet Synchronous Motor with Uncertain Time Delays via RBFNN
by: Shaohua Luo, et al.
Published: (2014-01-01) -
Real-time prediction of port water levels based on EMD-PSO-RBFNN
by: Lijun Wang, et al.
Published: (2025-01-01) -
RBFNN-Based Adaptive Fixed-Time Sliding Mode Tracking Control for Coaxial Hybrid Aerial–Underwater Vehicles Under Multivariant Ocean Disturbances
by: Mingqing Lu, et al.
Published: (2024-12-01) -
Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent
by: Linshen Li, et al.
Published: (2024-12-01) -
Optimizing learning outcomes: a deep dive into hybrid AI models for adaptive educational feedback
by: Hafiz Muhammad Qadir, et al.
Published: (2025-06-01)