Nonlinear System Identification using Spatio-Temporal RBF-NN

In this submission, I implemented RBF, Fractional RBF, and Spatio-Temporal RBF Neural Network for nonlinear system identification task
708 Downloads
Updated 5 Dec 2018

View License

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 (2024). 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. 1639–53, doi:10.1007/s00034-016-0375-7.

View more styles

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.

View more styles

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

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux
Categories
Find more on Sequence and Numeric Feature Data Workflows in Help Center and MATLAB Answers

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes
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

1.0.0