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Chaotic Time Series Prediction using Spatio-Temporal RBF-NN

version 1.0.1 (1.27 MB) by Shujaat Khan
Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

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Updated 05 Dec 2018

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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

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Shujaat Khan (2019). 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. 5311–5332, doi:10.1007/s00034-018-0835-3.

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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. 1639–1653, doi:10.1007/s00034-016-0375-7.

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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

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1.0.1

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MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Acknowledgements

Inspired by: Mackey-Glass time series generator