Nonlinear System Identification using RBF Neural Network
Updated 5 Mar 2018
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.
Khan, S., Naseem, I., Togneri, R. et al. Circuits Syst Signal Process (2017) 36: 1639. doi:10.1007/s00034-016-0375-7
Shujaat Khan (2023). 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 .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
Inspired by: Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks", Mackey Glass Time Series Prediction using Radial Basis Function (RBF) Neural Network
Inspired: 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!
Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.