Nonlinear System Identification using RBF Neural Network
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
Citar como
Shujaat Khan (2024). 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. Recuperado .
Compatibilidad con la versión de MATLAB
Compatibilidad con las plataformas
Windows macOS LinuxCategorías
- AI, Data Science, and Statistics > Deep Learning Toolbox > Function Approximation, Clustering, and Control > Function Approximation and Clustering >
Etiquetas
Agradecimientos
Inspirado por: Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks", Mackey Glass Time Series Prediction using Radial Basis Function (RBF) Neural Network
Inspiración para: Nonlinear System Identification using Spatio-Temporal RBF-NN
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Descubra Live Editor
Cree scripts con código, salida y texto formateado en un documento ejecutable.
html/
Versión | Publicado | Notas de la versión | |
---|---|---|---|
1.0.0.0 |