Nonlinear System Identification using RBF Neural Network
Nonlinear System Identification using RBF Neural Network
You are now following this Submission
- You will see updates in your followed content feed
- You may receive emails, depending on your communication preferences
In this simulation I implemented an RBF-NN for the zero order approximation of a nonlinear system. The simulation includes Monte Carlo simulation setup and the RBF NN code. For system estimation Gaussian kernels with fixed centers and spread are used. Whereas, the weights and the bias of the RBF-NN are optimized using the gradient descent-based adaptive learning algorithm.
Citation:
Khan, S., Naseem, I., Togneri, R. et al. Circuits Syst Signal Process (2017) 36: 1639. doi:10.1007/s00034-016-0375-7
https://link.springer.com/article/10.1007/s00034-016-0375-7
Cite As
Shujaat Khan (2026). Nonlinear System Identification using RBF Neural Network (https://www.mathworks.com/matlabcentral/fileexchange/66322-nonlinear-system-identification-using-rbf-neural-network), MATLAB Central File Exchange. Retrieved .
General Information
- Version 1.0.0.0 (47.3 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 |
