Mackey Glass Time Series Prediction using Radial Basis Function (RBF) Neural Network
Mackey Glass Time Series Prediction using Radial Basis Function (RBF) Neural Network
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In this submission I implemented an radial basis function (RBF) neural network for the prediction of chaotic time-series prediction. In particular a Mackey Glass time series prediction model is designed, the model can predict few steps forward values using the past time samples. The RBF is trained using conventional gradient descent learning algorithm and the kernel function is the Gaussian kernel with centers and spreads obtained from K-mean clustering algorithm.
Cite As
Shujaat Khan (2026). Mackey Glass Time Series Prediction using Radial Basis Function (RBF) Neural Network (https://www.mathworks.com/matlabcentral/fileexchange/66216-mackey-glass-time-series-prediction-using-radial-basis-function-rbf-neural-network), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired by: Mackey-Glass time series generator, Mackey Glass Time Series Prediction Using Least Mean Square, Mackey Glass Time Series Prediction Using Fractional Least Mean Square (FLMS), Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks"
Inspired: Nonlinear System Identification using RBF Neural Network
General Information
- Version 1.0.0.0 (658 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 |
