Chaotic Time Series Prediction using Spatio-Temporal RBF-NN
Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks
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Herein, you will find two variants of radial basis function neural network (RBF-NN) for chaotic time series prediction task. In particular, I implemented RBF with conventional and compared the performance with spatio-temporal RBF-NN for Mackey-Glass time series prediction.
* For citations see [cite as] section
Cite As
Shujaat Khan (2026). Chaotic Time Series Prediction using Spatio-Temporal RBF-NN (https://www.mathworks.com/matlabcentral/fileexchange/69523-chaotic-time-series-prediction-using-spatio-temporal-rbf-nn), MATLAB Central File Exchange. Retrieved .
Khan, Shujaat, et al. “A Fractional Gradient Descent-Based RBF Neural Network.” Circuits, Systems, and Signal Processing, vol. 37, no. 12, Springer Nature America, Inc, May 2018, pp. 5311–32, doi:10.1007/s00034-018-0835-3.
Khan, Shujaat, et al. “A Novel Adaptive Kernel for the RBF Neural Networks.” Circuits, Systems, and Signal Processing, vol. 36, no. 4, Springer Nature, July 2016, pp. 1639–53, doi:10.1007/s00034-016-0375-7.
Sadiq, Alishba, et al. “Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks.” 2018 3rd {IEEE} International Conference on Emerging Trends in Engineering, Sciences and Technology ({ICEEST}), {IEEE}, 2018
Acknowledgements
Inspired by: Mackey-Glass time series generator
General Information
- Version 1.0.1 (1.27 MB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
