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Nonlinear System Identification using Spatio-Temporal RBF-NN

version 1.1.2 (357 KB) by Shujaat Khan
In this submission, I implemented RBF, Fractional RBF, and Spatio-Temporal RBF Neural Network for nonlinear system identification task

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Updated 05 Dec 2018

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Herein, you will find three variants of radial basis function neural network (RBF-NN) for nonlinear system identification task. In particular, I implemented RBF with conventional and fractional gradient descent, and compared the performance with spatio-temporal RBF-NN.

* For citations see [cite as] section

Cite As

Shujaat Khan (2019). Nonlinear System Identification using Spatio-Temporal RBF-NN (https://www.mathworks.com/matlabcentral/fileexchange/68415-nonlinear-system-identification-using-spatio-temporal-rbf-nn), MATLAB Central File Exchange. Retrieved .

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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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. “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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Updates

1.1.2

- update citation information

1.1.1

- title change

1.1

- Comparison with conventional and fractional variant

1.0.2

- Simplification of code syntax

1.0.1

- Example added

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
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