Nonlinear System Identification using Spatio-Temporal RBF-NN

Version 1.1.2 (357 ko) par 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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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

Citation pour cette source

Shujaat Khan (2026). Nonlinear System Identification using Spatio-Temporal RBF-NN (https://fr.mathworks.com/matlabcentral/fileexchange/68415-nonlinear-system-identification-using-spatio-temporal-rbf-nn), MATLAB Central File Exchange. Extrait(e) le janvier 20, 2026.

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.

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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. 5311–32, 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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Inspiré par : Nonlinear System Identification using RBF Neural Network

Nonlinear_System_Identification

Version	Publié le	Notes de version
1.1.2	5 déc. 2018	- update citation information	Télécharger
1.1.1	26 nov. 2018	- title change	Télécharger
1.1	26 nov. 2018	- Comparison with conventional and fractional variant	Télécharger
1.0.2	15 août 2018	- Simplification of code syntax	Télécharger
1.0.1	6 août 2018	- Example added	Télécharger
1.0.0	6 août 2018		Télécharger

MLA	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.
APA	Khan, S., Naseem, I., Togneri, R., & Bennamoun, M. (2016). A Novel Adaptive Kernel for the RBF Neural Networks. Circuits, Systems, and Signal Processing, 36(4), 1639–1653. Springer Nature. Retrieved from https://doi.org/10.1007%2Fs00034-016-0375-7
BibTeX	@article{Khan_2016, doi = {10.1007/s00034-016-0375-7}, url = {https://doi.org/10.1007%2Fs00034-016-0375-7}, year = 2016, month = {jul}, publisher = {Springer Nature}, volume = {36}, number = {4}, pages = {1639--1653}, author = {Shujaat Khan and Imran Naseem and Roberto Togneri and Mohammed Bennamoun}, title = {A Novel Adaptive Kernel for the {RBF} Neural Networks}, journal = {Circuits, Systems, and Signal Processing} }

MLA	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.
APA	Khan, S., Naseem, I., Malik, M. A., Togneri, R., & Bennamoun, M. (2018). A Fractional Gradient Descent-Based RBF Neural Network. Circuits, Systems, and Signal Processing, 37(12), 5311–5332. Springer Nature America, Inc. Retrieved from https://doi.org/10.1007%2Fs00034-018-0835-3
BibTeX	@article{Khan_2018, doi = {10.1007/s00034-018-0835-3}, url = {https://doi.org/10.1007%2Fs00034-018-0835-3}, year = 2018, month = {may}, publisher = {Springer Nature America, Inc}, volume = {37}, number = {12}, pages = {5311--5332}, author = {Shujaat Khan and Imran Naseem and Muhammad Ammar Malik and Roberto Togneri and Mohammed Bennamoun}, title = {A Fractional Gradient Descent-Based {RBF} Neural Network}, journal = {Circuits, Systems, and Signal Processing} }