Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks"
Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks"
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This submission is a part of a paper titled as "A Novel Adaptive Kernel for the RBF Neural Networks" [1]. In this simulation I implemented function approximation problem. Function approximation problem is similar to the regression problem. Here in this case I used two input single output case, it can be easily extended to higher values.
Please Cite
[1] Khan, S., Naseem, I., Togneri, R. et al. Circuits Syst Signal Process (2017) 36: 1639. doi:10.1007/s00034-016-0375-7
https://link.springer.com/article/10.1007/s00034-016-0375-7
Cite As
Shujaat Khan (2026). Function approximation using "A Novel Adaptive Kernel for the RBF Neural Networks" (https://www.mathworks.com/matlabcentral/fileexchange/65709-function-approximation-using-a-novel-adaptive-kernel-for-the-rbf-neural-networks), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired by: Radial Basis Function with K Mean Clustering, Adaptive Fusion of Kernels for Radial Basis Function Neural Network, Adaptive Novel Kernel for RBF Neural Networks (Pattern Classification Problem)
Inspired: Mackey Glass Time Series Prediction using Radial Basis Function (RBF) Neural Network, Nonlinear System Identification using RBF Neural Network
General Information
- Version 1.0.0.0 (123 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 |
- Display picture
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